{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Long-Horizon Forecast"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Long-horizon forecasting is challenging because of the *volatility* of the predictions and the *computational complexity*. To solve this problem we created the [NHITS](https://arxiv.org/abs/2201.12886) model and made the code available [NeuralForecast library](https://nixtla.github.io/neuralforecast/models.nhits.html). `NHITS` specializes its partial outputs in the different frequencies of the time series through hierarchical interpolation and multi-rate input\n",
    "processing. \n",
    "\n",
    "In this notebook we show how to use `NHITS` on the [ETTm2](https://github.com/zhouhaoyi/ETDataset) benchmark dataset. This data set includes data points for 2 Electricity Transformers at 2 stations, including load, oil temperature.\n",
    "\n",
    "We will show you how to load data, train, and perform automatic hyperparameter tuning, **to achieve SoTA performance**, outperforming even the latest Transformer architectures for a fraction of their computational cost (50x faster)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can run these experiments using GPU with Google Colab.\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/Nixtla/neuralforecast/blob/main/nbs/examples/LongHorizon_with_NHITS.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Installing NeuralForecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install neuralforecast datasetsforecast"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Load ETTm2 Data\n",
    "\n",
    "The `LongHorizon` class will automatically download the complete ETTm2 dataset and process it.\n",
    "\n",
    "It return three Dataframes: `Y_df` contains the values for the target variables, `X_df` contains exogenous calendar features and `S_df` contains static features for each time-series (none for ETTm2). For this example we will only use `Y_df`.\n",
    "\n",
    "If you want to use your own data just replace `Y_df`. Be sure to use a long format and have a simmilar structure than our data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2016-07-01 00:00:00</td>\n",
       "      <td>-0.041413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2016-07-01 00:15:00</td>\n",
       "      <td>-0.185467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57600</th>\n",
       "      <td>HULL</td>\n",
       "      <td>2016-07-01 00:00:00</td>\n",
       "      <td>0.040104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57601</th>\n",
       "      <td>HULL</td>\n",
       "      <td>2016-07-01 00:15:00</td>\n",
       "      <td>-0.214450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115200</th>\n",
       "      <td>LUFL</td>\n",
       "      <td>2016-07-01 00:00:00</td>\n",
       "      <td>0.695804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115201</th>\n",
       "      <td>LUFL</td>\n",
       "      <td>2016-07-01 00:15:00</td>\n",
       "      <td>0.434685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172800</th>\n",
       "      <td>LULL</td>\n",
       "      <td>2016-07-01 00:00:00</td>\n",
       "      <td>0.434430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172801</th>\n",
       "      <td>LULL</td>\n",
       "      <td>2016-07-01 00:15:00</td>\n",
       "      <td>0.428168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230400</th>\n",
       "      <td>MUFL</td>\n",
       "      <td>2016-07-01 00:00:00</td>\n",
       "      <td>-0.599211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230401</th>\n",
       "      <td>MUFL</td>\n",
       "      <td>2016-07-01 00:15:00</td>\n",
       "      <td>-0.658068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288000</th>\n",
       "      <td>MULL</td>\n",
       "      <td>2016-07-01 00:00:00</td>\n",
       "      <td>-0.393536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288001</th>\n",
       "      <td>MULL</td>\n",
       "      <td>2016-07-01 00:15:00</td>\n",
       "      <td>-0.659338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345600</th>\n",
       "      <td>OT</td>\n",
       "      <td>2016-07-01 00:00:00</td>\n",
       "      <td>1.018032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345601</th>\n",
       "      <td>OT</td>\n",
       "      <td>2016-07-01 00:15:00</td>\n",
       "      <td>0.980124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       unique_id                  ds         y\n",
       "0           HUFL 2016-07-01 00:00:00 -0.041413\n",
       "1           HUFL 2016-07-01 00:15:00 -0.185467\n",
       "57600       HULL 2016-07-01 00:00:00  0.040104\n",
       "57601       HULL 2016-07-01 00:15:00 -0.214450\n",
       "115200      LUFL 2016-07-01 00:00:00  0.695804\n",
       "115201      LUFL 2016-07-01 00:15:00  0.434685\n",
       "172800      LULL 2016-07-01 00:00:00  0.434430\n",
       "172801      LULL 2016-07-01 00:15:00  0.428168\n",
       "230400      MUFL 2016-07-01 00:00:00 -0.599211\n",
       "230401      MUFL 2016-07-01 00:15:00 -0.658068\n",
       "288000      MULL 2016-07-01 00:00:00 -0.393536\n",
       "288001      MULL 2016-07-01 00:15:00 -0.659338\n",
       "345600        OT 2016-07-01 00:00:00  1.018032\n",
       "345601        OT 2016-07-01 00:15:00  0.980124"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from datasetsforecast.long_horizon import LongHorizon\n",
    "\n",
    "# Change this to your own data to try the model\n",
    "Y_df, _, _ = LongHorizon.load(directory='./', group='ETTm2')\n",
    "Y_df['ds'] = pd.to_datetime(Y_df['ds'])\n",
    "\n",
    "# For this excercise we are going to take 20% of the DataSet\n",
    "n_time = len(Y_df.ds.unique())\n",
    "val_size = int(.2 * n_time)\n",
    "test_size = int(.2 * n_time)\n",
    "\n",
    "Y_df.groupby('unique_id').head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# We are going to plot the temperature of the transformer \n",
    "# and marking the validation and train splits\n",
    "u_id = 'HUFL'\n",
    "x_plot = pd.to_datetime(Y_df[Y_df.unique_id==u_id].ds)\n",
    "y_plot = Y_df[Y_df.unique_id==u_id].y.values\n",
    "\n",
    "x_val = x_plot[n_time - val_size - test_size]\n",
    "x_test = x_plot[n_time - test_size]\n",
    "\n",
    "fig = plt.figure(figsize=(10, 5))\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.plot(x_plot, y_plot)\n",
    "plt.xlabel('Date', fontsize=17)\n",
    "plt.ylabel('HUFL [15 min temperature]', fontsize=17)\n",
    "\n",
    "plt.axvline(x_val, color='black', linestyle='-.')\n",
    "plt.axvline(x_test, color='black', linestyle='-.')\n",
    "plt.text(x_val, 5, '  Validation', fontsize=12)\n",
    "plt.text(x_test, 5, '  Test', fontsize=12)\n",
    "\n",
    "plt.grid()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Hyperparameter selection and forecasting\n",
    "\n",
    "The `AutoNHITS` class will automatically perform hyperparamter tunning using [Tune library](https://docs.ray.io/en/latest/tune/index.html), exploring a user-defined or default search space. Models are selected based on the error on a validation set and the best model is then stored and used during inference. \n",
    "\n",
    "The `AutoNHITS.default_config` attribute contains a suggested hyperparameter space. Here, we specify a different search space following the paper's hyperparameters. Notice that *1000 Stochastic Gradient Steps* are enough to achieve SoTA performance. Feel free to play around with this space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ray import tune\n",
    "from neuralforecast.auto import AutoNHITS\n",
    "from neuralforecast.core import NeuralForecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "horizon = 96 # 24hrs = 4 * 15 min.\n",
    "\n",
    "# Use your own config or AutoNHITS.default_config\n",
    "nhits_config = {\n",
    "       \"learning_rate\": tune.choice([1e-3]),                                     # Initial Learning rate\n",
    "       \"max_steps\": tune.choice([1000]),                                         # Number of SGD steps\n",
    "       \"input_size\": tune.choice([5 * horizon]),                                 # input_size = multiplier * horizon\n",
    "       \"batch_size\": tune.choice([7]),                                           # Number of series in windows\n",
    "       \"windows_batch_size\": tune.choice([256]),                                 # Number of windows in batch\n",
    "       \"n_pool_kernel_size\": tune.choice([[2, 2, 2], [16, 8, 1]]),               # MaxPool's Kernelsize\n",
    "       \"n_freq_downsample\": tune.choice([[168, 24, 1], [24, 12, 1], [1, 1, 1]]), # Interpolation expressivity ratios\n",
    "       \"activation\": tune.choice(['ReLU']),                                      # Type of non-linear activation\n",
    "       \"n_blocks\":  tune.choice([[1, 1, 1]]),                                    # Blocks per each 3 stacks\n",
    "       \"mlp_units\":  tune.choice([[[512, 512], [512, 512], [512, 512]]]),        # 2 512-Layers per block for each stack\n",
    "       \"interpolation_mode\": tune.choice(['linear']),                            # Type of multi-step interpolation\n",
    "       \"val_check_steps\": tune.choice([100]),                                    # Compute validation every 100 epochs\n",
    "       \"random_seed\": tune.randint(1, 10),\n",
    "    }"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-tip}\n",
    "Refer to https://docs.ray.io/en/latest/tune/index.html for more information on the different space options, such as lists and continous intervals.m\n",
    ":::"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To instantiate `AutoNHITS` you need to define:\n",
    "\n",
    "* `h`: forecasting horizon\n",
    "* `loss`: training loss. Use the `DistributionLoss` to produce probabilistic forecasts.\n",
    "* `config`: hyperparameter search space. If `None`, the `AutoNHITS` class will use a pre-defined suggested hyperparameter space.\n",
    "* `num_samples`: number of configurations explored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:torch.distributed.nn.jit.instantiator:Created a temporary directory at /tmp/tmp0ke5wzvj\n",
      "INFO:torch.distributed.nn.jit.instantiator:Writing /tmp/tmp0ke5wzvj/_remote_module_non_scriptable.py\n"
     ]
    }
   ],
   "source": [
    "models = [AutoNHITS(h=horizon,\n",
    "                    config=nhits_config, \n",
    "                    num_samples=5)]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the model by instantiating a `NeuralForecast` object with the following required parameters:\n",
    "\n",
    "* `models`: a list of models.\n",
    "\n",
    "* `freq`: a string indicating the frequency of the data. (See [panda's available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `cross_validation` method allows you to simulate multiple historic forecasts, greatly simplifying pipelines by replacing for loops with `fit` and `predict` methods.\n",
    "\n",
    "With time series data, cross validation is done by defining a sliding window across the historical data and predicting the period following it. This form of cross validation allows us to arrive at a better estimation of our model’s predictive abilities across a wider range of temporal instances while also keeping the data in the training set contiguous as is required by our models.\n",
    "\n",
    "The `cross_validation` method will use the validation set for hyperparameter selection, and will then produce the forecasts for the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:ray.tune.tune:Initializing Ray automatically.For cluster usage or custom Ray initialization, call `ray.init(...)` before `tune.run`.\n"
     ]
    }
   ],
   "source": [
    "%%capture\n",
    "nf = NeuralForecast(\n",
    "    models=models,\n",
    "    freq='15min')\n",
    "\n",
    "Y_hat_df = nf.cross_validation(df=Y_df, val_size=val_size,\n",
    "                               test_size=test_size, n_windows=None)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Evaluate Results\n",
    "\n",
    "The `AutoNHITS` class contains a `results` tune attribute that stores information of each configuration explored. It contains the validation loss and best validation hyperparameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.001,\n",
       " 'max_steps': 1000,\n",
       " 'input_size': 480,\n",
       " 'batch_size': 7,\n",
       " 'windows_batch_size': 256,\n",
       " 'n_pool_kernel_size': [2, 2, 2],\n",
       " 'n_freq_downsample': [24, 12, 1],\n",
       " 'activation': 'ReLU',\n",
       " 'n_blocks': [1, 1, 1],\n",
       " 'mlp_units': [[512, 512], [512, 512], [512, 512]],\n",
       " 'interpolation_mode': 'linear',\n",
       " 'check_val_every_n_epoch': 100,\n",
       " 'random_seed': 4,\n",
       " 'h': 96,\n",
       " 'loss': MAE()}"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nf.models[0].results.get_best_result().config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parsed results\n",
      "2. y_true.shape (n_series, n_windows, n_time_out):\t (7, 11425, 96)\n",
      "2. y_hat.shape  (n_series, n_windows, n_time_out):\t (7, 11425, 96)\n"
     ]
    }
   ],
   "source": [
    "y_true = Y_hat_df.y.values\n",
    "y_hat = Y_hat_df['AutoNHITS'].values\n",
    "\n",
    "n_series = len(Y_df.unique_id.unique())\n",
    "\n",
    "y_true = y_true.reshape(n_series, -1, horizon)\n",
    "y_hat = y_hat.reshape(n_series, -1, horizon)\n",
    "\n",
    "print('Parsed results')\n",
    "print('2. y_true.shape (n_series, n_windows, n_time_out):\\t', y_true.shape)\n",
    "print('2. y_hat.shape  (n_series, n_windows, n_time_out):\\t', y_hat.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/fz67du1y1Y8lhBBCCCGEaIOiCvUEPyqk6VoAWj0ByQwQ/qTVywQ0GRkZpKamcuTIEQoLC6moqCA4OJjIyEgGDRrEpEmT6NWrV0eO1SnPPvssixcv5o477gBgxYoVrFu3jldffZXly5c3OH7FihX1vn7qqadYs2YNX3zxBaNHj3YcM3v2bB5++GEAHn74Yb755htWrFjBqlWrOvcHEkIIIYQQQrSZ1AwQonGtDgZs3ryZxx57jNTU1BaPTU5O5sknn2TmzJltGlxrWSwW9uzZwx/+8Id6++fMmcOOHTucegy73U5paSlRUVGOfampqTzwwAP1jrvyyisbBBKEEEIIIYQQnqWlbgIXf08yA4Q/aVUw4K9//SuPPPIIiqIAajHBhIQEoqKiCAwMpKqqioKCAjIzM7FYLKSmpjJ79mz+/Oc/O66qd6a8vDxsNhtxcXH19sfFxZGTk+PUYzzzzDOUl5czf/58x76cnJxWP2Z1dTXV1dWOr0tKSgCwWq1YrZ67FkkbmyePUYjmyBwWvkDmsfB2MoeFJykoUz+Th5r1Tc7JsAB19XReaXWD+SvzWHgbZ+es08GAjRs38sc//hGAW265hbvvvpvk5GRMpoZrb6xWKzt37uTVV1/lgw8+4NFHH2XixIkuyxDQ6XT1vlYUpcG+xqxatYrHH3+cNWvW0LVr13Y95vLly1m2bFmD/evXryc4OLjFsbhbSkqKu4cgRLvIHBa+QOax8HYyh4UnyC0xADr2f7eD7B8bP+Z0ng4wkH72PGvXrq33PZnHwttUVFQ4dZzTwYAVK1ag0+l44YUXuOeee5o91mQyMXXqVKZOncqkSZO47777eO655zo9GBATE4PBYGhwxf7ChQsNruxfavXq1SxevJgPP/yQK664ot73unXr1urHfPjhh1m6dKnj65KSEhISEpgzZw5hYWHO/kguZ7VaSUlJYfbs2Y0GeoTwdDKHhS+QeSy8ncxh4Smqa+xUp24A4Ia5swkPanw+Rqbns/L4HnSBXZg7dwog81h4Ly0rvSVOBwN2795NfHx8i4GAS/32t7/l6aefdknlfbPZzNixY0lJSeGGG25w7E9JSeG6665r8n6rVq3iV7/6FatWreKaa65p8P1JkyaRkpJSr27A+vXrmTx5cpOPGRAQQEBAQIP9JpPJK15MvGWcQjRF5rDwBTKPhbeTOSzcraCyCgCDXkdUlyD0+sYze2PD1Mzdokprgzkr81h4G2fnq9PBgJKSEoYNG9amwXTr1o2DBw+26b6ttXTpUhYuXMi4ceOYNGkSb7zxBhkZGdx9992AesU+KyuLd955B1ADAYsWLeL5558nOTnZkQEQFBREeHg4APfddx/Tpk3j6aef5rrrrmPNmjVs2LCB7du3u+RnEkIIIYQQQrReXScBU5OBALi4gKAVu11p9lghfIXe2QPj4+M5cuQIRUVFrXqCwsJCjhw5Qnx8fGvH1iYLFixgxYoVPPHEE4waNYqtW7eydu1aEhMTAcjOziYjI8Nx/Ouvv05NTQ333HMP3bt3d2z33Xef45jJkyfzwQcf8M9//pMRI0awcuVKVq9ezcSJE13yMwkhhBBCCCFar9CJtoIAEcHqlVSbXaG0qqbTxyWEJ3A6M2Du3Lm8/PLL3HzzzaxatYrIyMgW71NYWMiCBQuorKxsNP2+syxZsoQlS5Y0+r2VK1fW+3rLli1OPea8efOYN29eO0cmhBBCCCGEcJWCCueCAQFGAyFmA+UWG4UVFsKDZVmA8H1OBwMeffRR3n//fVJSUkhKSmL+/PlMmzaNfv36ERUVhdlsxmKxkJ+fz4kTJ9i6dSsffvghpaWlREZGOjoRCCGEEEIIIYQrODIDQlo+uY8MMVNuqaSgwkJvQjp7aEK4ndPBgLi4ODZv3sz111/P6dOneeutt3jrrbeavY+iKCQmJvLZZ5/RrVu3dg9WCCGEEEIIIZxVWKH2W9dqAjQnKsTM2cJKRwBBCF/ndM0AgBEjRnDkyBFeeuklpk6dSkBAAIqiNNgCAgKYOnUqL730EkeOHGHkyJGdNX4hhBBCCCGEaFSBkzUDLj6mQIIBwk84nRmgMZvNjjX5drudM2fOUFBQQGVlJUFBQURGRtK7d2/0+lbFGYQQQgghhBCiQxXW1gxwNjPg4vsI4etaHQy4mF6vJykpiaSkpI4ajxBCCCGEEEJ0CO0qf4QTmQFaRwFtaYEQvk4u3wshhBBCCCF8Ul1mQMsFBKNqAwZSM0D4CwkGCCGEEEIIIXxSYbl6ld+pmgEhUjNA+JdWBwM2bdrEvHnzGDp0KBMmTODBBx/k3Llzzd5n/vz59O3bt82DFEIIIYQQQojWkpoBQjStVTUDnn32WX7/+98DattAgD179vDqq6/y8ssvs2jRokbvl52dzenTp9s3UiGEEEIIIYRwUpXVRoXFBtRd9W+OdBMQ/sbpYEBaWhoPPfQQiqIwaNAgZs+eTVVVFZ999hm5ubncfvvtnDx5kmXLlnXmeIUQQgghhBCiRdoVfqNeR2hAy6c9kbV1BYqkgKDwE04vE3jxxRex2WzMnz+f/fv38/zzz/P666+Tnp7Or3/9axRF4c9//jOPPvpoZ45XCCGEEEIIIVp0cScBnU7X4vGOAoIVFux2pVPHJoQncDoYsHXrVsxmMy+//DImU101zpCQEF599VXeeOMN9Ho9y5cv55FHHumUwQohhBBCCCGEM7Qr/M50EoC69oN2BUqqJDtA+D6ngwFZWVn069eP6OjoRr9/xx138N5772EwGPjrX//KH//4xw4bpBBCCCGEEEK0hpYZ4EwnAQCzUe9YTiB1A4Q/aFU3AbO5+T+k+fPn8/7772MwGHj66aclICCEEEIIIYRwi9Z0EtBESkcB4UecLiDYs2dPTp061eJx8+bNA+DWW2/l6aefbvvIhBBCCCGEEKKNLq4Z4KzIYBMZBVBYLssEhO9zOjNg1KhRlJSUsHPnzhaPnTdvHqtWrXJkCOzevbtdgxRCCCGEEEKI1igs1zIDnKsZAHWZAQWSGSD8gNPBgJkzZ6IoCv/85z+dOv7GG29k9erVGI1GrFaJrAkhhBBCCCFcp7C2gKCzNQPgoo4CUjNA+AGngwHXXXcdPXr0YNOmTRQUFDh1nxtuuIGPPvqoXvcBIYQQQgghhOhs7akZIJkBwh84XTOge/fuZGZmtvoJfvKTn1BdXd3q+wkhhBBCCCFEWzm6CbQiGKAFDiQzQPiDVnUTEEIIIYQQQghvUNjK1oIAEcFqRrO2xEAIXybBACGEEEIIIYTP0VL9o6RmgBCNkmCAEEIIIYQQwqdUWmxUWe0AREo3ASEaJcEAIYQQQgghhE/RigeaDDq6BDhdJk1qBgi/IsEAIYQQQgghhE8puKhegE6nc/p+Wn2BokorNrvSKWMTwlNIMEAIIYQQQgjhU7TMgNYUD4S6AoKKAiVVUkRQ+DYJBgghhBBCCCF8Sl1bQefrBQCYDHpCA9VlBYXlEgwQvk2CAUIIIYQQQgifUlTbGlCrAdAajroBUkRQ+DgJBgghhBBCCCF8ysU1A1pLu09hhWQGCN/W5mDA9ddfz0svvcShQ4c6cjwd4pVXXiEpKYnAwEDGjh3Ltm3bmjw2OzubW2+9lYEDB6LX67n//vsbHLNy5Up0Ol2DraqqqhN/CiGEEEIIIURbaFf1JTNAiKa1ORjw+eefc9999zF8+HDi4+P5+c9/zttvv82ZM2c6cnyttnr1au6//34eeeQR9u3bx9SpU7n66qvJyMho9Pjq6mpiY2N55JFHGDlyZJOPGxYWRnZ2dr0tMDCws34MIYQQQgghRBtpmQERbcgM0IoISmaA8HVtDgasWLGCn/70p4SHh5OTk8OqVau488476dOnD/369eOuu+5i9erV5ObmduR4W/Tss8+yePFi7rjjDgYPHsyKFStISEjg1VdfbfT43r178/zzz7No0SLCw8ObfFydTke3bt3qbUIIIYQQQgjPU5cZ0LoCggBRskxA+AljW+947733cu+996IoCnv27GHjxo1s3LiRb7/9lvT0dNLT03nrrbcAGDZsGLNmzeLZZ5/tsIE3xmKxsGfPHv7whz/U2z9nzhx27NjRrscuKysjMTERm83GqFGjePLJJxk9enSTx1dXV1NdXe34uqSkBACr1YrV6rkvLNrYPHmMQjRH5rDwBTKPhbeTOSzcLb9MDQaEBRhaPQ/DAw21j1EFQTKPhfdxds62ORig0el0jBs3jnHjxvHQQw9hsVhITU1l48aNbNq0idTUVA4cOMCPP/7Y6cGAvLw8bDYbcXFx9fbHxcWRk5PT5scdNGgQK1euZPjw4ZSUlPD8888zZcoU9u/fT//+/Ru9z/Lly1m2bFmD/evXryc4OLjNY3GVlJQUdw9BiHaROSx8gcxj4e1kDgt3ySkwADoO7dtN2fHW3ffseR1g4PiZc1w+SOax8D4VFRVOHdfuYMCl8vPzyczMJDMzk4yMDBRF6einaJFOp6v3taIoDfa1RnJyMsnJyY6vp0yZwpgxY3jxxRd54YUXGr3Pww8/zNKlSx1fl5SUkJCQwJw5cwgLC2vzWDqb1WolJSWF2bNnYzK1Pq1KCHeTOSx8gcxj4e1kDgt3UhSFB7/bCNi5dvYMekYGter+xkPnWZ2+H2NIBFAg81h4HS0rvSXtDgYUFxezefNmxzKBo0ePAuofYUhICFdddRWzZs1i1qxZ7X2qFsXExGAwGBpkAVy4cKFBtkB76PV6xo8fz/HjTYcZAwICCAgIaLDfZDJ5xYuJt4xTiKbIHBa+QOax8HYyh4U7VFhqqK6xAxAbHozJ1LpTnphQNXhQVFkDyDwW3sfZ+drmYMDDDz/Mxo0b2bdvH3a7HUVRMJlMXHbZZcycOZNZs2aRnJyM0djhyQdNMpvNjB07lpSUFG644QbH/pSUFK677roOex5FUUhLS2P48OEd9phCCCGEEEKI9tM6CZgNekLMhlbfX2stWCQFBIWPa/OZ+tNPP41Op6Nnz57cfPPNzJw5k6lTp7p9PfzSpUtZuHAh48aNY9KkSbzxxhtkZGRw9913A2oQIysri3feecdxn7S0NEAtEpibm0taWhpms5khQ4YAsGzZMpKTk+nfvz8lJSW88MILpKWl8fLLL7v85xNCCCGEEEI0rbBcPYmPDDG1aalwZG0woLjKis31K56FcJl2XbZXFIWzZ8+yZs0aysrKqKio4PLLLycyMrKjxtdqCxYsID8/nyeeeILs7GyGDRvG2rVrSUxMBCA7O5uMjIx697m4K8CePXt4//33SUxM5PTp0wAUFRVx1113kZOTQ3h4OKNHj2br1q1MmDDBZT+XEEIIIYQQomVaW8HI2haBrRURpKZYKwrUrhQQwie1ORjw7bffsmnTJjZu3Ehqaiqvvvoqr732GjqdjpEjRzrqBLgjW2DJkiUsWbKk0e+tXLmywb6Wihw+99xzPPfccx0xNCGEEEIIIUQn0oIBWrp/axkNesKDTBRXWimTlQLCh7U5GDBp0iQmTZrEI488QlVVFd9++62jiODevXvZt28fzzzzDCaTiYkTJzJr1iz+9Kc/deTYhRBCCCGEEKIerWZAWzMD1PuqwYByyQwQPkzfEQ8SGBjIrFmzeOqpp9i1axf5+fl89tlnzJs3D6vVyrZt21i2bFlHPJUQQgghhBBCNKlQCwaEtL0DgFY3oLym7e3JhfB0HVrqv7S0lC1btjgyBA4dOtSRDy+EEEIIIYQQzSrQlgm0IzNAu2+5LBMQPqxdwQCLxVJvecCePXuw2WyONfhGo5Hx48c7Wg0KIYQQQgghOt+F0ir+5z/7iQox8/SNIwg0tb7FnrcqrNC6CbRjmUDtfctkmYDwYW0OBsyePZsdO3ZQVVUFqEX4dDodI0aMcJz8T5s2jS5dunTYYIVoi39sS+e1b9Kxt1Ao0lX0Orh7el/umNrH3UMRF1EUhTP5FezLLCQto4i0zCKqrHbmje3JggkJhAW2PdVQCCGEcKULJVXc8uZOTuaWA1BttfPyz8dg0PtHyru2TKCtBQQvvm+51Xd+Z1VWG/d9sA+jQc+LN49G7yfzQTStzcGAjRs3AtC/f3/Hyf+MGTOIjo7usMEJ0V52u8KrW06SX/um4ClWf5cpwQA3K66wkna2iLSMIvZlFrI/s8hxJeFif1l7mBUbjnHTuARun9KbxOgQN4xWCCGEcE5OcRW3vrmT9Lxy4sICKCy38vXBHB5b8yN/uX4YOp3vnwBqBQQj2rFMICJYvQjgSwUE//zlIdYdPA/Ab2f2Y1C3MDePSLhbm4MBK1euZNasWfTo0aMjxyNEh/rxXDH55Ra6BBj56DeT0Lv5DTA9t5y7393T6EmncJ03t6bz1FeHuTRZxGzQM7RHGKMSIhiVEEGlxcbb357i2PkyVu44zb9STzN7cBx3TO3D+N6RfvGBSgghhPfILq7kljd2cjq/gh4RQXxwVzIHsoq55/29vL8rg5guASydPcDdw+x0hR1ZM8BHggFr0rJ4d2eG4+vUk/kSDBBtDwYsWrSoI8chRKfYcjQXgCn9oj3iBS80UP2TK6qwOJbWCNdSFIW3vz2FokCvqGDG9FJP/Ef3imRw9zDMxvpNVhaMT2D7iTz+se0U3xzLZf2h86w/dJ7hPcJZfFkSc4d3b3AfIYQQwtWyitRAQEZBBQlRQbx/RzIJUcEkRAXz5HXDePSzH3lh43Fiu5hZOKm3U49psyv8a8dpXvvmJF0CjGqwvPZ9c1C3hu+ZniA9t6yutWAHdBPIrdRhqbFj8uLVgiculPLwJwcA9bNPRkEFqSfzuX1KkptHJtytw7oJWK1W0tPTKSkpISwsjD59+mDy5r8a4RO2HL0AwOUDu7p5JCqt322NXaGsuoZQWYfucgeyiskuriLEbGD9A9NaLKik0+mY2j+Wqf1jOXGhlLe2n+aTvWc5kFXM/avT+NOaHxnVK1INKNRmFLSnYJEQQgjRWpkFFdzy5k7OFlbSKyqYVXcl0yMiyPH9XyQnkldWzYoNx/nT5weJDDFz7Yj4Zh/zxIUyHvr4B/acKQTgQmk16XnlfLIvCwCzUc/wHuGObLoRPcMJCWj+1CLQZKBLC8e0x9oD2Tz40Q9YbQp9YkPoFhbY5sca0TOcAKOeC1V2/t8Haby2cBwBRu8rwlhhqWHJe3upsNiY3DeapbMHMO+1VHadKsBuV6RugJ9r91/j7t27eeKJJ9i4cSMWS926bLPZzOzZs3nssccYP358e59GiFYrLLeQllkEwOUDY907mFqBJgOBJj1VVjtFFVYJBrjBuoM5gBogam1l5X5dQ1n+s+H8/sqBvL/rDO+knuFCaTVbj+Wy9Viu47je0cGOD0ejekUyND4Mk8Hzrp4IIYTwfhn5aiAgq6iS3tFqIKB7eFCD4+6b1Z/c0mre25XBA6vTiAw2M6VfTIPjamx23tx2iuc2HMNSY6dLgJGHrh5Ez8ig2jo7RezPLKK40sqeM4WOYIEzdDq4flQPfnflwHrBivay2uz89asjvLX9FAATkqJ46dbRGNvx3ts9PIjXfj6au975ns1H8/j1v/fw2i/GelVXBkVRePSzHzl2voyuoQE8f/NoIoJNhJgNFFdaOZxTwtD4cHcPU7hRu4IBb775Jvfcc0+9doKa6upq/vvf//L111/z6quvsnjx4nYNVIjW2no8F7sCg7qFNvqm6C6RwWayi6sorLCQEBXs7uH4nfW1hXPmDI1r82NEhZj5fzP78+vpfTmSXUpaZiH7MtUOBOm55ZzOr+B0fgWfpZ0DYEj3MP7728sk+i6EEKJDnc4r59Y3d3KuuIo+MSGsuiuZuCauhut0Op64bhiFFRbWHsjhrne+54O7JjG8Z93J4JGcEh786Ad+OFsMwLQBsSz/2XDHifuM2kxLRVE4lVfOvtruO2mZRRzOLqHG3nznJkWBT/dl8eWBbBZflsRvLu/b7m49OcVV/L/39/J9bVDi19P78Ps5A9sVCNBc1i+auwbbeeu4iS1Hc7nr33t4Y6H3BARWf5fJJ3uz0OvgxVtGExsaAMD4pCi2HM0l9WS+BAP8XJuDAWlpaSxZsgSbzcbkyZN58MEHGTFiBN27dyc7O5sDBw7w9NNPs2PHDn7zm98wduxYRo0a1YFDF6J539TWC5juIVkBmojaYECBh3U48AfpuWUcv1CGUa/rkKUjJoOe4T3DGd4znIWT1H0XdylIyyxk2/E8DmWXcPxCGQO7hbb7OYUQQng3m109kW5vy+OiCiv3rtpHTkkVfWNDWHVnMl1bSIs36HU8t2AUheXfkZqez23/3M1Hv5lMz8ggXtl8kpc2H8dqUwgLNPLYtUOYN7Zno/WNdDodfWK70Ce2CzeO7en0mH84W8RfvjzMrlMFvLrlJKu/y+T+K/pzy4Rebcqg23Eyj3tX7SOvzEJogJH/mz+SK4d2a/XjNGdAuMI/Fo7hzn/vY+uxXO741/e8uWgcQWbPDggcPFfMnz4/CMDvrhzIxD51Hd8m9Ylmy9FcdqbnS3crP9fmYMAzzzyDzWbj3nvvZcWKFfW+17t3b3r37s1PfvITli5dyooVK3juuef417/+1d7xik52/EIZORXqrcnY9PRIiAr26Kio3a7wTW3a9uUDPKNegCaytlVNkXQUcLn1h9SsgEl9owkP6pwlGuHBJqYPiGX6ADUI9Yt/7GL7iTx2ncqXYIAQQvi5A2eL+f1H+zmSU9phj9m/axfevzPZcdW3JQFGA28sGsvNb+zk4LkSFr29iy4BJg5nlwBwxeA4/nLDsCYzDNpjRM8IPrgrmQ2HL7D8q8Ok55bzpzUHWfntaf5w9SBmD4lzqriy3a7w6jcneWb9UUcW6Gu/GEvvmM5p/zsxKYp//WoCt/1zN9tP5PGrld/x1m3jCDZ3Xv2D9iipsnLPe3ux1NiZNagrd0/rW+/7k/qqgYFdpwqw2RUMkrnot9o8g7du3UpYWBh/+9vfmj1u+fLlvP3222zZsqWtTyVcaO6LOwAjy/fvaPa4AXFd+Oq+aR774nFxS8FxvSPdPZx6tOJyWtsb4Trra+sFzOngqwbNmZgUpQYD0gtY5GT1ZiGEEL6lymrjhY3HeX1rOja7QoBR3yGF9IbEh/HcglHEdHEuEKAJDTSx8vYJzHttB2fyK4BKIoNNPP7Tofx0ZHyndjvS6XTMHhLH5QNj+eC7TFakHCM9r5y7/r2HCUlR3H9Ff2Kb+Xlq7ArPrD/KhsNqkeibxvbkyeuHdfpFqglJUbzzqwn88u3dpKbnc/s/v+Pt28a3WDTR1RRF4aGPfnC0l3xm/sgGyxSHxocTGmiktKqGg+eKGdEzwj2DFW7X5tl7/vx5Ro4cidncfNXsgIAABg4cyP79+9v6VMKFIoNNWCwWzGZzk28ExZVWjp0vY9vxXI+p0n+pi1sKelrhNi0zoFAyA1zqQkkV+2oLSs4e3PZ6Aa2lpeXtOpXfrnaSmQUVxIYGeHRGjhBCiIb2nCnkwY/2czK3HIBrR3Rn2U+HEt3KE/iOFhsawL9/NZF7P9hHn5gQ/njN4FYHFdrDZNCzMDmR60fF89o3J/nHtlPsPlXArW/ucur+ZqOeJ68byoLxvTp5pHXG9Y7incUTue3t3ew6VcBt/9zNP2+f0KkdElpr5Y7TfPVjDiaDjpd/PoaI4Ibnaga9jolJUWw4fIHUk/kSDPBjbZ65ISEh5ObmtnwgkJubS3CwFErzBrsfnsHatWuZO3dGk60hl31xkH9+e5pVuzM8OBjgWS0FL6a1FyySzACXSjl8HkWBUQkRdAvv+NTHpoxMUFsT5ZVZOJlbRr+urV8q8N3pAha8nsrPxvTk/24a2QmjFEII0dEqLTb+b/1R3v72FIoCMV0C+PP1w7hqmOuy01rSKzqYz+6Z4tYxhAaa+P2Vg/j5xESeWX+MLUcvtFhPISEqmKduGM6wHq4vfjc2MZJ/3zGRhW/t4rvThSx6axe/u3IgOtyfLXuhtIqn1h4G4JG5gxmVENHkscl9otVgQHo+v57et8njhG9rczBg+PDhbNu2jS1btnD55Zc3edymTZs4ffo006dPb+tTCQ9zy4Re/PPb02w8fIELJVUtFqtxtaIKz2speDEtQutLmQH5ZdUcv1DmkucyGXQM7xGB2di6jI+O6CLQFgFGA2N6RZKans/O9II2BQM+TzuHXYENh8+3K7tACCGEa+xMz+ehj3+oTcGHn43pwZ+uHdLoVVqhiq9NafcGoxIieO+OifziH7vYm1HkdDaDq1wzvDu/nNy72WO0ugHfnSrAarN3eCatoiik55WTGBXcIZ0dROdoczBg4cKFbN26lZtuuokXXniBm2++ud4HVEVRWLVqFffddx86nY6FCxd2yICF+w2IC2VsYiR7zhTy4Z6z3DOjX9seSFHUhrMdbOvxPOwKDIzzrJaCmroCgr6RGWCpsXPliq3klbnu57llQgLLfzbC6eNLqqzsOJkH0OFVhp0xsU8Uqen57DpVwC+SE1t1X0VR2HJMzXQpqrCSnldO39gunTFMIYQQzaix2dlxMp+SquaD+TvT83l3ZwYA3cICWf6z4cwY5HmZiqJ9RvSM4P07k3nyv4fI96AOUYO7h/HUDcNavHAwuFsY4UEmiiutHMgqZkyvjq2xteHwBe5853sm9I7i7dvHe9RSClGnzf8rt99+O6tWrWLTpk384he/4MEHH2TIkCF069aNnJwcDh06xLlz51AUhVmzZnHbbbd14LCFu90yoRd7zhTywXcZ/GZ6X+f6p9ttkPMDnNwM6VsgcxfYLGAw126mult97b+NARCZCNH9IaZ/7W0/CGw6LaxuiYDnZQVA3TIBX2ktmFtWTV6ZBZ2OTj9JVRSFk7nlrP4uk7um9SXJyarBW47mYrUp9I0NccuJ9MSkaOA4u9JbXzcgPa+czIJKx9d7zxRKMEAIIdzgk31ZPPjRD04ff8uEBB6eO5iwwM7pXiPcb1iPcFb/epK7h9Em+tq6AesPnWdnen6HBwN2pucDsPt0Ab98ezcrbx9PqPwteJw2BwP0ej2ff/45999/P//85z/JysoiKyur3jEGg4HFixfz7LPPotdLeohPsJTDsa/5aU0RWwOzySzswvf7I5gwdBCYG6kLUZCunvinb4FTW6GysOExNVXq1pTstIb7QrpCdD81MBDdDyJ6QXgv7GE92VobDJjuocGACB9rLVhQmxHQNTSADUs7fznQ4pXfsfHIBV7YeJznFoxy6j7u6CJwsdG91GUNF0qrOZ1f4XQQA+qKYWr2ZhRy07iEjh6iEEKIFpzMVZfDxYcHkhDVdC2sYLOBxZf14bL+Ma4amhBtMqlvNOsPnSf1ZD5LLm9jpm8Tjl7UPnPPmUIWvb2bf/1qggTHPEy78jWCg4N54403eOyxx/j66685cuQIJSUlhIWFMWjQIK6++mp69uzZUWMV7mK3w5lvYf8qOLQGLGWYgRcAAoA1tZs5FLrEqifqwVFw/iAUnan/WAFh0Hsq9LkckqZBUISaHWCz1m6Wi24tavCh8BTkHYf8E+ptWQ6UX1C3jPotEPXAt4qJ7IAYem0fCD/2hPBeEDsQBl0LBvenKEX5WGvBvPJqAKJDXFOB+P4rBrDxyAXWpGVxz4y+La7Br66xOU6o3bFEACDQZGBUQgS7TxWwMz2/lcEANbg1oXcUu08XsPdMUSeNUgghRHMKazP6fp6c2PYlkkJ4EK1uwPenC7HU2Ftdj6k5R8+rwYAnrx/G/607yr6MIhb+YxfvLJ5IeJAEBDxFh5wZJSQkcOedd3bEQwlPUpAO+z9QgwBFGXX7I5MgdiCVhdnkX8gilmICdFawlEJBqXo/jd4IPSdA3xlqACB+TPtPyKtK1MCAFhwoOAlFmVB8FqU0mwCdld5kw6ns+vfrNgJ+8jz0GNO+528nrXhQhcVGdY2NAKN3t4rTMgOiu7imKNLwnuHMHhJHyqHzPL/xBC/eMrrZ43eczKesuoa4sABGuKHqsCY5KYrdpwrYlZ7PLROca4NUabGx61QBAPfP7s+tb+7i2IVSSqqsElkXQggXKyhXM/q0oL4Q3m5A11CiQswUlFv44WwR43pHdcjjFpRbyC1VLxbdMLoHY3pF8It/7GL/2WJ+8Y9d/HvxBCmm6SHcf5lUeJaqEnrlbcHwziuQubNuf0AYDL0eRt4KvZJBpyMI+H8vf0taZiF/mt2TX40MgbLaK/bleWrqfuIUCOjg9c2BYeoJfSMn9Te/8g1Zmek8PjWMK+ItUJypBjIOf67WK/jHLBh/J8x8VH0cNwgLNGLQ67DZFYoqrMSFeXcwIN+RGeC6F/X7r+hPyqHz/PeHc/x2Zj8GxDWdHeDoIjCkm3O1LTrJxD7RsOkEu04VOF03IDU9D0uNnR4RQUzqE03PyCDOFlayP7OIqf09cxmMEEL4qoLa97tIOYkRPkKv15HcJ4q1B3JIPZnfYcGAY7VZAT0jg+gSYGRofDjv35nMz/+xiwNZxfz8H7t4d/FEIiWw5naykF/UKUjH+PwQRme+jT5zJ+j00HcW3PgW/O4Y/PRFSJxUrwPALRMSAB3v7C1Eie4HvafA0Btgwp0w4MqODwQ0o6jCwneZZZxVujJk8lwYdQtMfxCuewn+3/cwfD4odtj9Orw8QV3y0EIf286g0+mIqE2P8oWlAloF3SgXLRMAGBofzlVDu6Eo8PyG400eZ7MrpBxyT0vBS43pFYnJoCO7uKpeQcDmaMsbLh8Yi06ncxT3kaUCQgjhelpLYMkMEL5kUh91qUBqbcG/jqAFAwZ1q7tYM7h7GKvuTCY6xMzBcyXc+o9dPlNM25s5FQwwGAzt3oxGSULweJFJENaD0sB4bDP+BA8chIWfwPB5YGq8Rd+1I+LpEmDkdH5Fh76ItMW2i1oKxkdcMt4uXeHGN2HhpxDVB0qz4T+L4P0FUHim8QfsRFoRQV94EXT1MgHN/bP7A/DlgWwOZ5c0ekxaZiF5ZdWEBhprK/q7T5DZwMieEUBdhd3mKIrCZkdnDLUl1Zhe6v33ZjRSiFMIIUSnKnAEv2WZlvAdWt2APWcKqa6xdchjasUDL83cHNgtlA/uSiamSwCHs0u49c2d5JVVd8hzirZx6gxd6YCrpx3xGKKT6XTULPovm7bsZu7kazCYWn6zCwkwct2oeN7blcGq3ZlM7uu+yrmbnWkp2Hcm/GYHbHsGtq+A4+vglW1w+R8geYna1tAF1BTDcp/oKKBlBrhymQDAoG5hXDOiO1/+kM2KDcd4feG4Bsesq10iMGtQ1w4titNWE/tE8f2ZQnaeymf++OY7AmgtBc0GPZNr36jHJKqZAfsyCrHbFbcuexBCCH9SY7NTXKm+Z8syAeFL+sZ2ITY0gNzSavZlFJHcp/0XT7TMgIHdGi7j7B+nBgRufXMnR3JKueWNnVwzonu7n9OVugQYuWNqH3cPo0M4FQw4depUo/s/++wzHnzwQfr06cO9997L0KFDiYuL4/z58xw8eJAXX3yR9PR0/va3v3Hdddd16MBFJwmJrbcMwBm3TOjFe7syWPdjDgXlFrekz9ntCluPqSnVLbYUNAWpNQOG3wT/fUDtlJDyJ9j7Dgy5DvpfCT3Hgb7z1vJH+lBHAUcwoIvrlglo7p/Vn7UHsll38Dw/ZhUz7KICgYqisM7NLQUvNTEpmpc3n2RXekGLx2pLBMYnRRISoL5UD+4eRqBJT0lVDel5ZS12UhBCCNEximoDATodUgld+BSdTkdyn2i+2H+O1JP57Q4GKIrSZGaApl/XLnxwVzK3vLmT4xfKWNHMkk9PFBcW4F/BgMTExAb7UlNTefDBB1m0aBFvvPFGvWJYAwcOZNq0afzmN7/hrrvu4ve//z0TJ05s9HE6wyuvvMLf//53srOzGTp0KCtWrGDq1KmNHpudnc3//M//sGfPHo4fP869997LihUrGhz38ccf89hjj3Hy5En69u3LX/7yF2644YZO/km8w7Ae4QzvEc6BrGI+2XvWLX8cB8+VkFdmIcRsYFyik8VPYgfCbV9C2nuw/lG1O8G2Z9QtKAr6XaHWPeg7U22V2IEia5cJ+ERmQG16lzuCQP3jQvnJiHg+33+OFRuO849f1mUHnLhQzpn8CsxGPdMHeEaxvbGJkRj0OrKKKjlbWEHPyKb7VGstBS8f0NWxz2TQM6JHhKPFoAQDhBDCNbS2guFBJowG92eaCdGRkvtEqcGA9HweaOdj5ZRUUVJVg0Gvo09s062U+8R24aO7J/PuzjOUW2ra+ayuFepDHZ3avJD/qaeeIjAwkBdffLHZqtjPP/88H3zwAU899RRr1qxp69M5bfXq1dx///288sorTJkyhddff52rr76aQ4cO0atXw3Ze1dXVxMbG8sgjj/Dcc881+pipqaksWLCAJ598khtuuIFPP/2U+fPns337diZOnNjZP5JXuGVCLw58eoD3d2ew+LIkpyqldyTtxGlKv5jWpYPrdDD6FzDoWji2Tl02cGIDVBbAgf+om06vtkccMAcGXwcx7e8trKUYFvpCzQA3LRPQ3DurP//94RwbDp/nh7NFDI5T33jWH1bnxNR+MY4r6+4WEmBkRM9w9mUUsSu9gJ5jGw8GXNxS8NJlL6MTa4MBGYUtLjUQQgjRMerqBcgSAeF7tCKCaRlFVFltBJranh2rZQUkxYS02D47ISqYh+cObvNzifZrc2hz165dDBo0iMDAwGaPCwoKYtCgQaSmprb1qVrl2WefZfHixdxxxx0MHjyYFStWkJCQwKuvvtro8b179+b5559n0aJFhIc33oN8xYoVzJ49m4cffphBgwbx8MMPM2vWrEYzCPzVT0fFE2w2kJ5bznenXV/cbMsxrep61xaObEJQBIxcAPPeht+nw+1fwZT7oesQtQNB5k7Y+AS8NA4+WwLFZ9s1Xq23aqGXZwZUWmxUWNRiM64uIKjp17UL14/qAcBzKccc+zfUBgPc3UXgUlohw+aKCF7cUrBf1/odORwdBaSIoBBCuIwjGCD1AoQPSooJIS4sAIvNzt4z7ft84agX0EzbZ+E52hwMKCsrIzc316lj8/LyKCsra+tTOc1isbBnzx7mzJlTb/+cOXPYsWNHmx83NTW1wWNeeeWV7XpMX9MlwMhPR8YDsGp3hkufu6jCwr7aE6Nmiwc6y2CExMkwexksSYX7D8A1z6ptFlHUZQUvjIH1j0Fl214w65YJeHdmQH5tz2WzQU8XN159/+2s/hj0OjYfzSUts4jCavjxXAl6HVwx2MOCAX3UJSfalf/GXNpS8GJaMOD4hTJKqrw7mCSEEN6ioPb9WvqiC1+k0+k6rMXg0Rz1nK+pegHCs7T503u/fv04ePAga9euZe7cuU0et3btWk6fPs2wYcPa+lROy8vLw2azERdX/8N/XFwcOTk5bX7cnJycVj9mdXU11dV1rTJKStTWZ1arFavVcz/Aa2NryxhvGhPPB99l8uWBbP541QBH+7zOtuXIeewK9O8aQmyIseN/vyHdYdQiGLUIXdb36DctQ5+RCjteQNn7L+yT78c+/k4wNp8lc7HQADUOl19e7dHzoSXniyoAtc1STY371nv1DDdz3cjufLLvHM9vPEGcTT2BHtMrgrAAvUf9jkfGh6LXQUZBBRl5pXQPrz9vFEVh85HaJQ59oxqMPSJQT8/IIM4WVvL9qTym9nNfBw/RedrzWiyEJ/C1OZxXUgVARFAnfM7wVtWlUJqNrjRbvS3PRQmOgfAeKGE9ISy+VZ+NGn384rPoijPRFZ+FEvXfFJ9FV1UEeiPojSh6g/pvnUEt/qztj0hEGXELSvzoVhfH1vjaPG7OhN4RfJZ2jh0n8rh3Rtvrfx3NUc95+sYE+cXvzVM5+7tvczDg9ttv53/+539YsGABjz/+OHfffTchIXVFIioqKnjttddYtmwZOp2O22+/va1P1WqXXklTFKXda9hb+5jLly9n2bJlDfavX7+e4OCmi4Z5ipSUlFbfR1GgR7CBrAo7y1dtYHp317STfP+EHtCTYChl7dq1nf+EUXcTZ5zEkHP/IazqLIZNy6je9iJHut9IZtQUtcZAC06UABjJyi1yzZg7ycFCHWDAaKty+88xFPgMA9tPFhBhVv8PepLv9nE1pmewgYxyHW9+tplxsfX/Ti5UQmahEYNOoeTE96xtpJlLnEHPWfT8Z+N3lB6Ttq2+rC2vxUJ4El+Zw3tPq581CrIzWbv2jLuH4zqKQnjlGeKK0wixXCDIUkCgtZBAayEme1WLd68yhlFpjqbSFE2lOZoqUzh6pQaD3YrBXl13q6i3ersVk62cYEs+Zlu5U0Ns9hP+3n9SHJhARvR0MqMmYzV2ae7oJvnKPG5OZRWAkbTMQj79Yi0BbSgbYFfgaI4B0JF9ZA/+9KfiaSoqKpw6rs3BgHvvvZf169ezbt06HnzwQR599FESExMdrQUzMjKorq5GURTmzJnDvffe29anclpMTAwGg6HBFfsLFy40uLLfGt26dWv1Yz788MMsXbrU8XVJSQkJCQnMmTOHsLCwNo+ls1mtVlJSUpg9ezYmU+uv7BfFZvL4F4c5UBHGX6+e3OmFBO12hSf//g1g4barxjtSnDrfNWB/iJoD/8HwzXKCS88xJuNNRlduxzb1dyj9rwJj0632jl8o48WDO7DqzMydO8NFY+54Vfuy4MhBkrrHMHfuWHcPh6P6g/xnTxZFFnXe/fZn0+kV5XnBtx/0R3nr2zNYInoxd+7Qet9bmXoG0o4yMSmaG34yrtH75+/MYM+XRygP7OoRv3fR8dr7WiyEu/naHN700QHIzmbc8EHMvay3u4fTuRQFzv+I/vAa9IfXoCtsvMU4gBIQBqHdUcLiITgaKvJrr+JnobNWEFhTQmBNCZE0/RjNDiUwAsITUMJ6oIQnQHhP9TY4Cux2sNeAYlNv7dptDdis6E9vRXfkC8KrMhme9S7Dcj5EGXQt9lG/QEl07uKNr83j5iiKwlvp2zhXXEXM4Altyjw8k1+Bded2Aox6Ft5wNQa9awuKizpaVnpL2hwMMBgMfP755/z5z3/m+eefp6SkhGPHjnHsWF0Br7CwMO69914ee+wxDIbO69muMZvNjB07lpSUlHpt/1JSUrjuuuva/LiTJk0iJSWFBx6oa7axfv16Jk+e3OR9AgICCAhoeDJoMpm84sWkreP82dgEnv76GMcvlHMgu4yxzrb5a6Mfs4odLQWT+3bF1JpOAu1mgnGLYORNsPsN2PYMutzDGD9ZDEGRMGwejLoF4sc0SE+LDVNPUIurrOgNRq99sSyqVIsHxoQGesS8/u2sAXyadg6rTWFQXBf6xjVeFNTdJveL5a1vz7D7dFGD39u2E2otgRmD4pr8nY5PUt+g084WYzAY0Xvp/BEt85b3DCGa4itzuKhSXQrnKe93Ha42AMDBz+Dgp1Bwsu57xkDoPxviR0NovJr+HxYPod3RBahX2hu8CymKWlep+OxFWyaU54HRDMYgMF20GQPBFAymQDCHQnhPCO+JLjCs8cd3xrhF6hgOfAR7/oXu/AF0Bz9Gf/BjiEyCMQth5C3qz9ICX5nHLZnUN4aP957luzPFzBzcvdX3P5lfCUD/uC4EBkh9DXdydr62q+KXyWRi2bJlPPTQQ2zfvp0jR45QWlpKaGgogwYN4rLLLnN5SvzSpUtZuHAh48aNY9KkSbzxxhtkZGRw9913A+oV+6ysLN555x3HfdLS0oC6oohpaWmYzWaGDBkCwH333ce0adN4+umnue6661izZg0bNmxg+/btLv3ZvEFYoIlrR3Tnwz1n+fW/9zha6HUWrYDa5Na2FOxIpiCYch+MWQSpL8O+d6E0G757U91iB6lvNiMWQJj6wqrVU1AUKKm0em1BIne3FbxUQlQwN4/ryb93ZXLtiNa/ibnKuN5R6HRwKq+cCyVVdA1T11RWWmyOLgPNFcMc1D2UIJOB0qoaTuaW0V+K9AghRKcqrC0g6K7OOR2uqgTyj0PecTh/EI6uhfwTdd/XAgBDb4D+V0JAK9PrdTr16n1wFHQf0bFjb42gSJhwJ4y/A7LTYO878MOHUHhK7RK18UnofRkMvwmG/FQ93o9N6hvNx3vPknqybUUEj9W2FZTigd6jQ8p/BwcHM2fOnAYV991hwYIF5Ofn88QTT5Cdnc2wYcNYu3YtiYmJAGRnZ5ORUb/a/ejRox3/3rNnD++//z6JiYmcPn0agMmTJ/PBBx/w6KOP8thjj9G3b19Wr17NxIkTXfZzeZNFk3rz8d6z5JVZyCtzTbV8jzjxC4qEmY/C5Q9D+mZIWwVH/gu5R2DD/8LGZdB3Joy8BVPfmYQGGCmtrqGwwuK1wQDt/zfKgz4c/fHqgYSXnmbxlER3D6VJ4UEmhnQP4+C5EnaeKnB04miupeDFTAY9I3qGs+tUAXszCiUYIIQQnSy/9v2usy9ydLjyfMj5QT3pzzsGeUfVf5dmNzzWEFAXABhwJQT40HuLTqdmNsSPhjl/hkNr1MBARiqc3qZuX/4P9J8Dw+fBgKvA7HnLDDtbcm3HowNZxZRV17S6U9QRaSvoddzXC6wTLVmyhCVLljT6vZUrVzbYpygtF+CaN28e8+bNa+/Q/MLwnuGkLJ3OhZLqlg/uAKGBRobGe1AdBr0B+l2hblXFaspd2vuQuRNObFA3YIsukiOmeLps+hr6jFIzCGIHQYir6h60X0Fta0FPyQwAMBr09A9XMBrclCnipOQ+0Rw8V8Ku9HxHMEBrKTi9kZaClxqTGKkGA84UsWB8r04frxBC+DMtMyDKg97vmlRjgWNfq589jq9X19Q3pkscxAyA6H5qS+UBV0GgB32e6izmEBh1q7oVnoEfP1aXElw4CEe/VDdzFxh0LbohNxBcfV4NoOgVsFkdNQmwW9VbgISJrc+e8EA9I4NJiAois6CS704VMGNQ11bd35EZ0E2CAd6iQ4IBpaWlnDx5krKyMux2e5PHTZs2rSOeTniBvrFd6Bvr/S+K7RYYDmN/qW75J2H/B/DjR1CQTjSFTDEUwuGDcLhu2QrBMdB1MIxeCCPmt7kdjivkO5YJNF0sUTRuYlIUb20/5VgWoCiKIxhw+YCmlwhoxvRSUxn3ZhR23iCFEEJQZbVRYVFPqD02k09RIHu/GgA48CFUFtR9L6qverEhpr968h8zAGL6+X1KPACRiTB1qbqdP6gGBQ58BMUZ8MMHGH/4gNkAh1p4nLAecM2zMPAqFwy6c03qE01mwVl2pue3KhhQXWPjVJ7aAUIyA7xHu4IB+/bt43e/+x3ffPNNi1fXdTqdW/uQC+F20X1h5iPqVl3K429/RtnZA9wx0MIgwzl1OUHRGajIq0tZ2/sOXPN/anDAA+V74DIBbzEhSa0bcDK3nNzSakqrrGQUVGAy6JjsRAXf0b0iALUzRXGllfAg3y9sJIQQ7qBlBRj1OkJbmTbd6cpy4YfVahDgwsG6/aHd1VpFo26F2IHuG583iRuqbrP+BJm74cCHKIc/x1ZRjMEciM5gAr0JDMba29qvy3OhJAtWLVCLR1/9NIS0vhK/p5jUN5r/fH+WHa2sG3Aqr5wau0JogJHu4YGdNDrR0dr8irZ//36mTZtGRUUFiqIQEBBA165d0es9OzVXCI8QEEpR5DA+OxPFwN6DGTStj7rfUq6moh1PgW3PwJnt8NplkLwEpj/kcSlo+bXLBGIkM6DVIoLNDIwL5UhOKbtPFXC+RO3XPL53lFNr9GK6BJAYHcyZ/ArSMouY7kQ2gRBCiNbTiuVGhpg7vWWyU6pK4OhXanr7yY1q2jqoa/4HXQOjfg59Z6jLFkXr6XTQayL0mkjNnOWsXbuWuXPnNl2d3VIBW5ZD6ktq9ufJTWpAYPhNHp3d2ZTJfdVAxo/nisktrSY21LnPeEcvWiLgEX8nwiltDgb87//+L+Xl5UyePJkXX3yxXhE+IUTLImqLEGlXHAB1HVv8KHUbuQC+flgtQrjjBfVN/6rlMPinHvHmUmGpocqqLguSzIC2Se4TzZGcUnadyud0fgXQfBeBS43pFcmZ/Ar2nimUYIAQQnSSwnJ1XXiUO4sHWirUOgAHP4Fj68F2UV2mHuPUDIBhP5PUf3cwB8OcJ9XCi5//Vm3R+Mmd8MN/4NrnICLB3SNslbiwQEb0DOeHs8VsPnKB+eOdG/8xrXig1AvwKm2+jL99+3YCAwP57LPPJBAgRBtEOoIB1sYPiOgFN78Ht/4HIhLVFLT/LIJ3b1TrD7iZtkQgwKgnxCxXH9pCq9q79VjuRS0FnV+fN6Z2qYDUDRBCiM6jZcG5vHhgTTUc+RI++hX8vR98dDsc/kINBET3VzMG79kNd26E8YslEOBuPcbAXVvUzlIGM5xIgVeSYfeb0ExNNU80a1AcACmHzzt9n6M5ZYDUC/A2bc4MqKioYMCAAcTEeO+aGCHcKTJETTcrqmih/eKAKyFpGmx7Fr5doaYEvpIMU+6Hyx5wW+ubuuKBHpI26YUmJKmdI7SsgB4RQfRvpqXgpUbXFhFMyyzCblfQ6+X/Qfg+S42d0qomgqi19Dqd5xZ6E16nsNyFnQRKstU085Mb1SWD1SV134tIVK/+D/0ZdBvuEVmC4hIGE0z7PQy+Ts0SyNwJa3+nFnXsN1vtGBUcrRaLDo5WawsERXrcko4rhnTluQ3H2HY8lyqrjUBTy+PTMgMGSDDAq7Q5GNC7d28sFtf0kBfCF2nLBLS1iM0yBamFB0ferL6pnNwEW/+mFhi8/CG184DBtQXkHG0Fu0i9gLaKCjEzIK4Lx86r0XRnWgpebFC3UILNBkqrajiRWyZvwMLn5ZZWc/Xz28gra7l17W2Te/P4T4e6YFTC1xXUZvBpQfwOZa1Se92f3AgnNtUvAggQGl8XAOgxRgIA3iJ2ANz+FXz/Fmx4HDJ3qVujdGpAIKyHutxjzCK314ga0j2M+PBAzhVX8e2JPGYNjmv2+ApLDRkF6oWNAXGeVd9KNK/NwYAFCxbw5JNPcuLECfr169eRYxLCL0QGa5kBzV/hqie6L/ziEzi0BlIeg6IM+O8DsONFmPGI+mHBRUU888q8qOeyB0vuE+0IBjjTUvBiRoOeET3D2ZlewN4zhRIMED7vH9vTnQoEAGw9ntvJoxH+wpEZ0FE1A8py1UJzJzbC6e1QU3nRN3UQPxr6zoT+s6HnBJe9r4sOptfDhDthwFWw799Qcg4qCtSuUeV5UJEPVUWAoraCrCyAdQ+rF3sm3AUTfq1mEriBTqfjiiFxvJN6hg2Hz7cYDNA+x8R0CZCLRF6mzcGAhx56iM8//5z58+fz0Ucf0adPn44clxA+L7KxAoLO0Olg6PUw8GrYsxK++RsUpMPHi9VlBLP+F/pd0elXDwouWiYg2m5iUjTvpJ5xuqXgpcb0ilSDARmF3DyhVyeMUAjPUFxh5d3UMwD8Y9E4rhjS+IfTY+dLmfPcVscJnBDtVVBR102gXWw18P3bsOnPUF1ct79LN+g3Sw0A9JnhthNA0UkiEmDGHxv/ns0KlYVqcCBzp3pxpyAdvnkavn0BxiyESf8PIhNdO2bgisFqMGDj4QstLkU8lqMVD5SsAG/T5mDA3/72N2bMmMGLL77I0KFDufLKKxk4cCAhISFN3udPf/pTW59OCJ+jfagoqrCiKErr190bA2Dir9UWQjtfVTsO5ByA9+ZBr8lwxf9Cr+ROGLkqv0xbJiDBgPaYMSiWqf1jGNMr0qmWgpcaU1s3YG9GUQePTAjPsnLHacotNgZ1C2XW4KYLbWqB1qJKKza7gkFqaYh26pCaAZm74cul6vs0QNxwGHGTGrzvOkTS//2VwQRduqpb3BAY80s4/DlsXwHZabD7DfjuLRh2I0y5D7oNc9nQJvZRWx1fKK3mQFYxIxMimjz2qNQL8FptDgY8/vjj6HQ6FEUB4PPPP2/yZEY70ZFggBB1tGUCFpudCouNkDacCALqurLpv4dxv4Ltz6pVazN2wNtXqqlp436lXm3o4JoC+Y4PR5IO1h7BZiP/XjyxzfcfXdtR4MSFMoorrIQHu7Z2hBCuUF5dwz93nALgnhn9mg2eRtT+DSiKWqBVUlZFe2mZcJFtWSZQngcb/hf2vat+HRgOs/4EY2/3uKJxwgPoDWqLwiHXw6lvYPtzkL4FDvxH3frMUD/T9UqG7iPVC0OdJMBoYPqAWL48kM2Gw+ebDQY42gpKMMDrtDkYsGjRIqkgLkQ7BJkMmI16LDV2CissbQ8GaEKi4cq/QPJv1PSyfe+qPYmPfQ1BUeqby4j5Hbb+UGstKJkB7hXdJYDe0cGczq9gX2Zhq1oTCuEt3tt1hqIKK0kxIcwd3r3ZY00GPeFBJoorrRSUSzBAtF9BWzID7DbY80/Y+ARU1S4JGP0LuGKZWkFeiObodNDncnU7tw++fV6tF5W+Wd0ADAFqUcmEiWpwIGEiBEd16DCuGNKVLw9kk3LoPP8zZ2CTxx2tXSYwoJsEA7xNm88+Vq5c2YHDEML/6HQ6IoNNnC+pprDcSs+Oag8c3hN++iJMvlfNEjj4CZTnqhVtv38LwnvB8Bth+Hw1Ja2NpGaA5xjTK5LT+RXszSiSYIDwOVVWG29uU7MCfjO9r1Np/1EhZkcwQIj2UBTFUdvH6WDA2T3qkoDsNPXrbsNh7jPQq+1ZYMKPxY+Gm1ZC/kk48l/I2KXWF6jIVztRZKTCt7XHxgyAHuMgdiDEDlK7GkQktjkL5fIBXdHr4EhOKZkFFSRENWxnXVhu4UKpunRUlgl4n3ZeihRCtEdksFkNBrS2iKAzYvrD3L/BlU+pqWYHPoLDX0Bxhpp2tv056DoUBswBgxkUeyObot6G9YCxv4SAuhd5RzBArrq53ejESD7Zl8W+jEJ3D0WIDvfhnrPkllYTHx7I9aN7OHWfqBAzp/LKJRgg2q2sugarTV0S2+QyAUVRawEcXw/HU2pbyCkQEA4zH1WX6xnkI7dop+i+at2AKahzLv+kGhTI2KnOubxjddvFjIEQ3V8NDMQMVAMFXYdAdL8WM0UjQ8yM6x3F7lMFbDx8ntumJDU4Rlsi0DMyqE21j4R7yf+YEG7U5o4CrWEwqlWK+82Ca59Vlw0c+Ej90HLhYMOexk35dgVMfwjG3oaiNzrae0lmgPuNqa0bkJZR1GLFXyG8idVm57UtJwH49fS+mI3OLXHSXlsLOvO1VfiFwnK1/W+QyUCQ+aKrq1XF6lru4+vh+AYoy6l/x5G3wOwn1MJwQnQ0nQ5i+qnb6F+o+8rz4exuyP4B8o5C7lHIOw41VXD+gLpdzNwFuo2A+FHQfZR6G92vQRbB7MFx7D5VwIbDF5oNBki9AO8kwQAh3CgyRC10VVRhdc0TmoLU2gFDb1Bb2Rz6HLL3q28qOj3oDLW32te1/z70ORSchLW/g52vUD3tUaprAgCd1AzwAAPjQgk2GyitruH4hTIGypo94SPWpJ0jq6iSmC5mFoxPcPp+WpCyoEyCAaJ9Cios6LEzNKhEveqfcwBObFSvyNpr6g40Bavru/tdAf1nQ4S0ehUuFhKttp0eeHXdPrsNis6ogYHco2rWQO4ROH8ILGVqwemMHXXHm0Kg+wg1OJAwHnpP44ohcfxl7WF2ncqnpMpKWGD9QsWOTgLy2cMrORUMMBjUCNGgQYM4ePBgvX3O0ul01NTUtHygEH4kwhWZAU0JilRT/50x4xHY+y/Y8lcoSCfws1/xmbkvzyg/J9h8TeeOU7TIaNAzsmcEqen5vLktnRE9w5s8NjTQyNXDuhNokirWwrPZ7AqvbDkBwOLL+rRqzmqtWyUzQLSKpaI2zfo45B+HvGP0yzrMoYCTBFqs8N4lx0f3h/5zoP8VkDilUyu7C9EmegNE9VG3i4MEthp1rmenwbk09TbnAFjL6+oQ7HoVgKSuQ3kmtA9rywew42Bfrhpbv5CgVjxQMgO8k1PBAK19oHZ76b9b8xhCiDpae0GXZQa0lcEE4++AEQsg9WVs259nVM1J/s0T8N4uuOLxdhUjFO03NjGS1PR8Ptpzlo/2nG322OIKa6OpfkJ4knUHc0jPLScs0Mgvklt3lVXLDCiUmgHCWcc3wIe3gaW03u4uADqwYsLUtb9ajyfxMjUAENXHHSMVov0MRvVzW9wQGHWrus9uUwME59LUDgZnvoXzP8KFg9zIQW40g+2L52DvGEiaBn2mo/ScUNdJoDOCAdWlcGYHXDgM5hC1dlW9Lazu38ZANZtVtIpTwYBTp9QqviaTqcE+IUTbuaRmQEcKCIXL/8C3YT/h9Cf/y8+NGzEcXwcnUtQ3k5mPQWg3d4/SLy2anEheWTWlVU1nYB09X8qJC2VkFFS6cGTiYtnFlezPLCK3tvJyU2w2G9UVLhqUB1IUhZc3q1kBt03uTeglaakt0TID8iUYIJyRtQf+s0i9KhoUpVZhj+kPMQP4OieUv3xnY+yIEay4ZZy7RypE59EboOtgdRt1i7qvPA9ObeXCDymUHdlEH30OZH2vbtufBYOZf9iT2G0aQr+yQLBMAnPDjgNOq6mGzN1q4etTW9W/TbuTmeV6EwRHq1tIdN2/L966xKl1EUK7SeCgllPBgMTERKf2CSFaR1sm4G0Vr3NsYfyp5nYO9rqVpyM+U3vf7nsXfvwUpj4Ak/6fWp9AuEzX0ED+euOIZo95Y+tJnlp7xHuCT16uvLqGA1nFpGUWsS+jkLTMIs6XNB8EuFiE2cCvOnF8nmzL0VwOnish2Gzg9jZksTgyA2Sui5bkn4T35quBgL4z4ZbVYKyrhbP/6yNkKie5oou8pwk/FBIDw35G9JAbuOovGwgsP8c7M6vpV7YHTm1FV3qOCfqjTOAovP+pekLeYyz0ngK9L4OEieoVfbsd7FawWcCm3db+uyIfTm9XT/4zdkLNJRcsInur7RLtNWqmQHUpVJdc9O9SQFEfvyynYTHPxphD1e4M0f3UwF90v7otoEtn/CY9lhQQFMKNvGaZwCXyytUTmpqIvjD/Hcj8Dr7+gxop3vRn2PMvdenAsBsl8upBvDX45ClO5ZXzxf5z2OzNL3u7UFrFvowijp0v5dJD9ToY2C2MxKjgJv80rDaFDYfPU2TRYbXZMbXuorjXUxSFl2qzAn4+sZfjKn9rREoBQeGMslx490aoyIPuI9X3M2P9+aYtNYlqqq2gEH7AoNcxY2BXPt5rYZUlicd+dhcoCqvXfcOebf/lxqhTTNQdhpIstbhm5k7Y9kxdMWpnr+4DhHSFPtMhabq6HCGyhQvQdrsazKssgsoCNbhQod3mq9kN2r+Lz6oFFS2lap2E7LSGjxcQprbcNgaqrwfGQLUeiCFAvTUGQEgsXP9KK36DnqvNwYD777+fK664gmnTphEWFtaRYxLCb0R66dUr7QO2o5NAwni4Y4PasnDD41CcCR8vht1vwJXLoedY9w22MXY7KHa/6/usfZgt8rL55ike+vgHdp8qaNV9uoUFMiohgtG9IhiVEMHwnuEEm5ufdza7Qr9H1qIoUFJpJTjQv4qS7TpVwJ4zhZgNeu6c2rY12dFSQFC0pLoM3r8JCk9BRCLc+qG6FO4SWvC0LUEpIXzJ7CFd+XjvWTYcPs+j1wxGp9PxXWkkH9lm0GPkr5k4qx8UnlZrDZzerm7FmernrUvpDOoJt8GsZpL2GOuoQ0DsoNZdSNLr6+oGRDjRdabGov7d5x2H/BO1xUJrbyvy1ayDloTGOz8+D9fmT8IvvPACL774IgaDgbFjxzJz5kxmzZrFlClTCAjwrw8uQrRVpOPkzLsyA7QPR9EXfzjS6WDETTDoGkh9WV1LlrkL/jFTLTw4638hvAcoCpRdUFsVFqSrW37tv4vOqF0OYgbUbbED1dvgqMYHY7erV3VKzkFpNvrCTAZmf4t+/XY18ltVovaDriqGau22FPRGGPwTGPcrtQq0H2QwSIX1tiurrmHPmUIAbh6fgNHQ9HzpEmBiVEI4oxIi6RYe2OrnMuh1hAUaKa6soaiyhm6RbR62V9JqBdw0riddw1r/+4O6uV5ltVNhqWkxACP8jM0KH/5SLZIWHA0LP4XQuEYP1YL1URIMEH5uav9YzAY9Z/IrOJlbRr+uoRyrbSs4sFsX9XNUVJK6jf6FeqeyC2phQoNZLUat3erd2NHIaFY/W8YObPi9igK19XZNNdRUqbe26tqvq+u+NvjO60G7MgM2b97MDz/8wK5du9i9ezd//etfCQgIYPLkyY7gwPjx49Hr9R05ZiF8hrZMoKy6BkuNHbPRO/5W8sqb+XBkDobpv1ffCDY+Afvfhx9Ww6HP1bVYhafU3rZNqSpWI8vH19ffHxyjBgWi+qgn+SXZUJoNpTnqOrFaBmAQQEtLxmwW+PFjdYsdpAYFRiyAoIiWfwEl2XXFbSzl0CtZDSjEDVMj1B5Km2+F5d4VfPIEu0/lY7Mr9IoKbrE2Q0cIDzKpwQA/C9zszyxi2/E8DHodd0/v2+bHCTEbMBv1WGrsFJRbJBgg6igKfH4vnNgApmC49T/q2uEmODIDZJmA8HMhAUYm94tmy9FcUg5doE9MF0cwoMlOAl26unCEHSA4qumLTz6qze+Ozz77LAD5+fls3ryZjRs3snHjRk6cOMGmTZvYvHkzjz32GKGhoUyfPp1Zs2Zx7733dtjAhfAFYYEm9DqwK1BUaaFraNuugrlaQW3NgJguzWQBhXWHG16FCXfCuj+qPWvPH1C/p9NDeIJ6Yh/dt7YHbl91XVhFPuQere31fAxyj0HJWfXqf0YeZOxo5Ml06htOaHfsXbpxptBCrwEjMQRHQGB47RYGgRHqWrDAcDWQsOef8MOHkHsEvnpQXeIw7EYYvxjiR9c9fGVRbXGbbyD9G8g7Wv/pD32m3gaGQ6/JauGcxCnQbYRHLUXQgjfeFnzyBN+eyAdgSr9olzxfRJCJDCopqnR/4OZMfjn/Tj2DxdZIqmcH+/60mn1x3ah4EqLaXpFap9MRFWwmp6SKgnILPSPbUd1a+JZNT6pBap0BbloJPZvvEFBYm7knmQFCwBWD49hyNJcNh88zd3g3qqzqZ4nE6BB3D020Ubs/pUZHRzNv3jzmzZsHwNmzZx2Bgc2bN5OVlcUXX3zBl19+KcEAIS6h1+sIDzJRWGGlsNzqNcGA/LJWpE32GAO3f6WeTFvK1ZP/iF5qAZam9L6s/tfVZepartxjatZAYLgabAiNV2+7xKlpZ4DNauWHtWvpOWMuhuYqr4XGQfzzMPsJ+OE/8N1bkHsY9v1b3eLHQOJkNYhxbt8la950arGpPpera9TO7FCXRFQVw7Gv1A3UarW9kqHvDBj8U+fWsnWiesGnCkubU7D90bcn8gCY3DfGJc8XUZvFUewBwYAXN53goz1nXfZ8Oh0subztWQGaqJC6YIAQAOx+Uy1qBvCT52HAlc0ebrMrjmUCkSF+VslTiEbMGtyVRz+DvRmFjiB5/65dMOh9f6mlr+rwS1bdunWjb9++nD59mlOnTnHu3DkURUFRmq++LIS/igw2q8EAL0kHVhTF0bvbUUCwJTodJE1t+5MGdFGv1F98tb6jBIar2Qvj71BP/L9/W22VeG6vummi+6kn/0nT1WDFpWlkthrI3g9ntqvBgTOpao2CEynqtu6P0HM8DLkehlznlsCAXq8jIthMQbmFAgkGOC2vrJojOWoq5KS+rskMCA/ynE4j50uqALhqaDcGdGsiFbQDjewZTr+u7X8eLVgpwQABwOEvYO3v1X/PeATGLGzxLsWVVrSPr7JMQAjoHh7E8B7hHMgq5s1t6QAMbGqJgPAKHRIMSEtLc2QDbNu2jYqKCsfJ/6BBg5g1axazZs3qiKcSwudEhpghr9xr1gZrKeYA0SE+VCxUp1MzARInqx0Q0t5T6xskTFQDAOE9mr+/wah2Teg5FqbcpxbMOf+jmhFx5Es1QHD2O3Vb/0jLgQFrpVp4pzxXva3IV4+LH60GMNooMthEQblF6ga0QupJ9erHoG6hzS+N6UARWjDAAzIDtEDlgvEJzBjkPes/JRjg56yV6utu+mY4uaVumdrY22Da7516CG3uhAUaMRlkWZUQoGYHHMgq5lReOYBLgsSi87Q5GPD66687lgIUFBQ4Tv4TEhKYN2+eIwDQvXv3Dhuss1555RX+/ve/k52dzdChQ1mxYgVTpzZ9VfKbb75h6dKlHDx4kPj4eB588EHuvvtux/dXrlzJ7bff3uB+lZWVBAbKlTXRPo6ibh5wBdAZ2oejIJOBILMbq8F2pi6xcNn97XsMvUFdStB9JEy6Ry10eOhztb7ApYGBHuPU5Q5luVB+Qb21lDbxwDq1kGKPseoSjJ7joOvQBr2xmxIVYuZkbrnXZKJ4gh21wQBXLRGAuswAT1gmoAWOtKUL3kKCAX7Gboec/XBysxoAyNilVv2+2IgFMPcZp7vHSCcBIRq6YnAcKzYcd3wtmQHerc3BgN/85jdqgZ6oKG688UbHyX+/fv06cnyttnr1au6//35eeeUVpkyZwuuvv87VV1/NoUOH6NWrV4PjT506xdy5c7nzzjt59913+fbbb1myZAmxsbHceOONjuPCwsI4erR+0TAJBIiOEFGbeugtJ2d5Za1cIiBUod1g4l3qdmlgIOt7yGrkPoYAtTBiSKzacjH/hNp+Me+ouu1/v+647iPUoMKYRRA3pMlheNt88wQ7Tqr1AlxVPBAuqhlQUeOy52yKt54QaeOVue5DaqrVNrIlWVB8Vt20f5/9Tm0JdrGwHtBnhrrEq8/0Vlc2d3QS8LK5L0RnGhofRvfwQLKL1SVkkhng3dq1TEBRFCorKykpKXFs7vbss8+yePFi7rjjDgBWrFjBunXrePXVV1m+fHmD41977TV69erFihUrABg8eDDff/89//d//1cvGKDT6ejWrZtLfgbhX7TMAE9YG+wM7cNRtHw4artLAwPHvlb7XofE1p78d1WzEwLCGl7BKs+DrD31t8rCukyD3a+rAYEZjzT6wTdKCwbI1VKnnC2s4Ex+BQa9jglJrms3pC0TKKx07/9TdY2NCosNqAskeQvtBE4reCq8TEVBbYr/Jjh/SD3hL7/Q/H3MoWp9Gi0AENPf6SyAxmivk1FeNveF6Ew6nY4rBsfx751nCA0wEh8uF0e9WZuDAe+99x6bNm1i48aNrF+/npSUFAAiIiK4/PLLHZkCAwcO7LDBtsRisbBnzx7+8Ic/1Ns/Z84cduxorB0ZpKamMmfOnHr7rrzySt566y2sVium2mrkZWVlJCYmYrPZGDVqFE8++SSjRzddzKy6uprq6rr0NC1QYrVasVo996RPG5snj9HXhAWoqfb5ZVVe8XvPLakA1CCGJ47X6+ZwYDSM+Hnj36tp5KqwORySZqobqD2zC0+hO7cX/ZH/oj/6X9izEuXAR9inPIB9wq/BWPdGHRaozre8Uu+Yb+629ah68jGiRxiBBtfNqxCzuj65qMLi1v+n3NrigQa9jiCD4lVzJjxA/R0WlFd71bh9Ratfi+016LL2oEvfhC59M7pz+9DRsPi0YgyEsHiUsJ4Q1gMlLB4lrAfEDkLpPtrRWQZo/DW0FfJK1fkfHmSUOeSnvO4zhYtcPbQr/955hnG9I6hp59+Z6BzOztk2BwNuueUWbrnlFgBOnz7Nxo0b2bBhA1u2bOHTTz/l008/RafT0b17d2bNmsXMmTP55S9/2danc0peXh42m424uLh6++Pi4sjJyWn0Pjk5OY0eX1NTQ15eHt27d2fQoEGsXLmS4cOHU1JSwvPPP8+UKVPYv38//fv3b/Rxly9fzrJlyxrsX79+PcHBnt/vWAvuiM539rwOMHD01FnWrs1w93BatCNLHW9F4QXWrl3r7uE0yf/mcDAEzyeq/0iGZb1PZMUpDJufpPrbVzkUP5+siImg03G+9v/v4PHTrF2b7u5Be7yPj+sBPbH2QpfO99OlAEZyCkrd+nd2rlwdR5DezldffeW2cbTF8WJ1rmecd+3/naivudfiQEsBcSX76VpygNiyQxhtFfW+XxLYkwuhwyjoMoAKcwyV5mgshi71r/aX127ZefBDx77uf39a/fsvyvGO92fRefzvM0XLfj8CIs058vrqoSoqKlo+iA7qJtC7d28WL17M4sWLATh48CAbN25k3bp1fP3117z77ru89957nR4M0OguSQlTFKXBvpaOv3h/cnIyycnJju9PmTKFMWPG8OKLL/LCCy80+pgPP/wwS5cudXxdUlJCQkICc+bMISwsrHU/kAtZrVZSUlKYPXu2IytCdC79wfOsTt+POTSKuXMnuHs4LUr76ihknGHkwD7MvXKAu4fTgMzhuaDcR82PH2HY/GeCS88x7vQrjOmxG/vsP1PRrTufZxwkOLIrc+eOcfdgPZqiKPz5wDeAhYVzJpDcx3XLBI7nFPPcj7uwYGTu3OZ7oXemXacK4Ifv6RrRhblzp7htHG1x/HwZLx3agVVvZu7cGe4ejt9p8rW4OBP9kS/QHf4CfdZ39e6jBEWiJE3H3mcmStIMgsK6kwgkunboDls+PgDZ2YwZNpC505LcNArhTvKZQngrZ5fvd0gw4GIHDhxwtBncvn07UHdy3dliYmIwGAwNsgAuXLjQ4Oq/plu3bo0ebzQaiY5uvFiUXq9n/PjxHD9+vNHvAwQEBBAQ0LAFlclk8ooXE28Zpy+IDQsC1BZi3vA717oexIYFevR4/X4Oj/k5DLsBUl+G7c+hz/oe/cqrmJEwl/G6cZRUjPPv348Tjp8vJbfMQoBRz/g+MZhMruueEVP7ulBWbQO9wW1tzUqq1TaiUSFmr5svseFqFl5RpRW9wYhB3/a146LtTCYTppIMOLQGDn8O5/bVP6DnBOg/G/rOQhc/Cp3egKc08SuqVNOfu4YFed38Fx3L7z9TCK/j7HxtdzDg1KlTjpP/TZs2kZenVl3WAgDx8fHMmDGDWbNmtfepWmQ2mxk7diwpKSnccMMNjv0pKSlcd911jd5n0qRJfPHFF/X2rV+/nnHjmv6grCgKaWlpDB8+vOMGL/xWZG1hIm8pIJjvKCDomn7roh3MwTD99zBmIWz6M+x7l26Za/kwYC35+RHw35/B4J9C78vqr7MVAHx7Qn0/G987ikAXBgIAwgLr/j+KK63EdHHP35tWid8bq6lrxVkVRa29EO2m36HfURQoyUJ3/ggDcj7D+ObTcOFg3fd1ekicAkOug0HXqm1VPVRB7fuyN85/IYRwRpuDAXfeeScbN27kzJkzQN3Jf1RUFNOnT3fUCRg0aFDHjNRJS5cuZeHChYwbN45JkybxxhtvkJGRwd133w2o6ftZWVm88847ANx999289NJLLF26lDvvvJPU1FTeeustVq1a5XjMZcuWkZycTP/+/SkpKeGFF14gLS2Nl19+2aU/m/BNdd0ELNjtCnoPv3qlVeaOktaC3iO0G1z3Eky4i9Itz2M/spZoXRF8/7a6BUXCwGtgyE/VCtxGOWkC+PZkPgCTXdhSUKMV7Ku06SiqcF8wQAtSaq9T3sRo0BMeZKK40kpBuQQDOpylXG13mndc3fK12xNgrcAIDNaO1RkgaVpdAKBLrBsH7jxHN4EQ75v/QgjhjDYHA9566y0AQkJCuOyyyxwn/6NHj252fX5nW7BgAfn5+TzxxBNkZ2czbNgw1q5dS2KiuuIsOzubjIy6IjBJSUmsXbuWBx54gJdffpn4+HheeOGFem0Fi4qKuOuuu8jJySE8PJzRo0ezdetWJkzw/PXdwvNp7brsCpRW1RDu4R+6pbWgF+s+gpqfvsqE/V8xSX+Qf07MwXD0S6jIg7R31S0gDPpdAd2GQ+xAiBkIkb3B0OGryjxajc3OznQ1GDClb4xbxhBihEqbGih0F+1kKNJLW6tFh5gdwQDhJGsV5B5W256WXVC3cu02F8rOQ1kuVBc3/Rh6I0pkb7JrIug6dRHGIT+BYNfV3Ogo3j7/hRCiJW3+dPf4448zc+ZMkpOTMRo960PikiVLWLJkSaPfW7lyZYN906dPZ+/evU0+3nPPPcdzzz3XUcMToh6zUU+I2UC5xUZBhcWjgwGKopBfrrbMlKts3iksyIRNZ2SrfST5M5fS9dpnISNVXct76HMoy4GDn6ibxmCGqL4QOwBiB0HMAIgfDdF93feDdLIfz5VQWlVDaKCRYT3C3TKGYCNQ7d4lRFqNkAgvPRmKDDFDXrkEA5pit0PeMcjaU7ed/xHsTrYKC46BmP4Q3a/2tr/6+hCZSI0dvlu7lrkj54IXrrW21NgprVZ/D1ES/BZC+Kg2n8X/6U9/6shxCOHXIoLNlFsqKaywkESIu4fTpNLqGqw2dUmQZAZ4J4NeR0SwmYJyC4XlVrqGBkLSVHW76mk4+x2c3gq5xyDvqJr2a61QrxTmHgbW1D1Y35kw5T5Iml6/1ZcP2HFSrReQ3CfabYXngo0KoKOo0n3BAC0rwRuXCUDdSVyBG7MrPIrNCic2QMZO9cT/XBpYShseFxwNEYnQpSuExKq3XeLq/7tLVwhsJlBm9446OE3R6mXodfVreAghhC/xrEv6QvipqBAzWUWVbk0HdkZBbb2AELPB5QXVRMeJCDapwYBL55teD70mqpvGbofiTPXqYe5RNUCQe1QNGpzcpG7dR6pBgcHXOb+coCgTznwL5hDoO0stduhBdpzQlgi4vl6AJrj2V+nO1wXtJNpbMwOiasetvXb5raoS2PsO7HwVSs7W/54pWM306TEGeoxVt/AEnwvwtVbBRUsEPL2WjxBCtJUEA4TwABG1V90Kyz37Soq2RECKB3q3qGAz6ZQ71sM2S6+HyER16z+7bn/haUh9RT3ByN4PH/1KvZI4+bcw6ucNT+7L89WMg/Rv4NQ3UJBe9z1TMPSfA0OvV2/N7s2OqbLa+O50AQBT+rmnXgCAVrPMncsEtOf21jRp7bXKbzMDis/Crtdgz7+gurbndEgsDJwLPcepJ/4xA/2uJogz6ooHeufcF0IIZ8irvxAeQCtO1OBKrYfROglIW0Hvpl3lbdcJUmRvmPs3mP4QfPcP2P06FJ2Btb+DzU/BhLvUK42ntqon/zkH6t9fZ1CvRpZfgKIMOPSZupmC1aDDkOthwJVuCQzszSikusZO19AA+nXt4vLn1zgyAyrdWEDQ25cJaHPd32oGZO+HHS+ptT+09f8xA2DS/4MRC8AU6N7xeYECL26rKYQQzpJggBAeoK69oKdnBkgnAV+gtcnqkPkWEg2XP6RmBKS9BzteVIMC3/y14bFdh6j1BfpMV/uMB4apPcnP7VMDAQc/U+97aI26GYPUwMDoheqti9KWtSUCk/tGu7U7jlozwH2vCza7QnGldxcQdNQM8KVggN2u1vGwlIOlrPa2dqvIV7uCnNpad3zvqerfZ7/ZaqaPcIojM8BL574QQjhDggFCeIAIL8kMcLQVlGUCXi2yM06QzMEw4U4YezscXgM7X1NbFiZOgT6Xqz3Gu3RteD+drnat8hi4Yhlkp6lBgUOfqUsRDn+ubt1GwLTfwaCfdPoJzbe1xQMnu3GJAKitBQHHCbmrlVRaUdR4hGMpk7fx2mCAokBptppRk/ND7e2Pals/SzmgNH9/nQGG3gCT/5+agSNaraB22Z5kBgghfJkEA4TwAFpmgKcHA/LKamsGyDIBr+ZYltIZJ0gGIwy7Ud1aS6dTT1ziR8MVj6upzj/8B/asVE+I/rNIbW049X9g6M86ZZ1zaZWVH86q/dMnu7F4INQtE3DX64L2vKEBRkwG77yirAUDOmWud6TiLLWgpuPE/4B6lb9ZOjB3UZfSBNTemkKg51iY8GuISHDJ0H2VNv+1TCohhPBFEgwQwgNEOj6wevYygQJZJuATorwhE0Wng/hR6jbtd2oV9F2vQ+4R+OROtS7B1KUw4mYwdtx83H2qAJtdITE6mJ6R7u1wEOLmZQLa/Ijw4pMhLRiQX25BURS3LvtowFoJh/+rpvWnf0ODq/06A8QOhLhh0G24ukUmgjm09sQ/yO8r/nem/Iu6CQghhK9ySTDgtttuo7S0lI8//tgVTyeE1/G6AoKyTMCrOZYJeHiNCofgKJj5iJryvPtNSH0ZCk/B57+FLU/DZferdQU6oCjat456Ae5dIgB1mQHF7goG1AYnvXnNtBYMqK6xU2m1EWx28zUQRYGz36sBgB8/qavwD9BjnLpcRjvxjx0shf7cSLoJCCH8gUveFb/88ksKCgpc8VRCeCUtGOAtBQTlw5F3cyxL8fTU6UsFhqtZAsm/ge//qRYrLDmrdjDY9qz6vdEL25UpsKO2XsCUfu5dIgB1NQNKq2uw2uwuT9V3ZAZ4cTAg2GzAbNRjqbGTX2YhOMpNwYCSbPjhA0h7H/KO1e0P7wWjboVRt6gdOoTH0DLhpGaAEMKXyTIBITxAhJfUDMivrRkQ00VqBngzx7IUD59vTTKHqFkC4+9Qr7Bue04NCny5FL5dAdP/oLZPa2VNgbyyao7klAIwqY/7gwFBFw2/uNLq8r87LTjprW0FAXQ6HdEhZrKLqyissJAQ5cKlH9Wl6jKAAx9C+mZQ7Op+YxAMuQ5G/xwSL5MK/x5Ke32UZXFCCF8mwQAhPEDkxamsFhtBZoObR9SQoigXFVSSD0feTEv7Lq1yzxXnDmMKVAMCoxfC3ndg69+hKAPWLIHtz8LlD6uFBp082dpxUl0iMLh7GNEeEPDS6yAs0EhJVQ1FFa4PBvhCZgComVfZxVWOzKZOVVMNJzaoAYCjX0FNVd33ek1SswCGXK+21RQeS1GUuswAL5//QgjRHAkGCOEBQswGTAYdVptCQYWFHuYgdw+pgZKqGqw2tcCVBAO8W1iQCZ1OXb5cVGElNtT9J77tYgxQ2xqO+jl89w/Y/hzkn4CPF8O2Z2DGIzDomhaLraVqLQXd3EXgYuFBJkqqaiiudH0WR6EjM8C7/961GiedtizGblc7ARz4DxxaA1XFFz15fxh+EwyfB9F9O+f5RYertNqorlEzOeT9TgjhyyQYIIQH0Ol0RASbyS2tprDcQo8IzwsGaEsEugQYCTR5XuaCcJ5BryMiyERhhZXCCov3BwM05mCYci+Mux12vqbWFLhwCFb/XG1XOO33MODqJjMFtOKBnlAvQBMZbCKzsNItnUYKHWumvXeZANQFMwo6OhhQdkENPu39N5Seq9sf2l1trTn8Jug+Uir+eyFtrpiNeoI9MFNPCCE6igQDhPAQUbXBAE8tIuhoKyidBHxCZIiZwgprx58geYKAUJj+e5hwB+x4SW1LeG4ffHArxAyEKfepJ2oXFRrMLKggo6ACo17HhCTPCQaEB6kn4kWVbggGVPhGmrR2ZbfD5vr5g5D6ipoJYKt9zMBwtQ7A8JsgcQro5QTSm2lzJSrY7FntKIUQooNJMEAID+HpRQTzyqRegC9RT/DKKfLQ+dYhgiJh1mNq94EdL8L3b0PeUbWmwOa/QPISGPtLCAh1dBEYmRBBlwDPeWt0BAPc8P9U5CPLBDokGGC3q7UAUl+CU9/U7e85Xp1fg65Vl6sInyCdBIQQ/sLpTzzvvPNOm5+kurq6zfcVwl/UtRf0zJMzR2aAfDjyCXWp056ZidKhQmJg9jKYulRtSbjzFSjJgvWPwNa/k550K68cHg2EMKVfjLtHW48WJCx2Y2ZAhBd3E4B2BgMsFbB/lZpdkn9c3afTw+CfwqR7IGFCB45UeIq6YrnePfeFEKIlTgcDbrvttjanSimKImlWQrRAW5db6KHLBLSaAdEhcvXLF0R6eCZKpwgMh8vuV6/k7v8A5dvn0RWcpM/hV1inmNgeMYPLLP3gS6taBb6mGmoqa29rvzYFwchbYegN9ZYZdJaIoEb+nxQFsvfD4c+hskhNSdcb1ZNUvQF0hrpbgxGi+0GPsRCe4PT6dUVR6jIDvDwA2OpggKUc0reo3QCO/BcqC9X9AWEwZhFMuAsiEztnsMIjaEHSKHm/E0L4uFblQiqK0lnjEMLvae27PPXkTGvLFSU1A3yCdoLUaRXWPZkxgNwBN3Pf9/0Is6znbuPnjNKnc0XVevhufcv3T98CG/5XPSkcexsER3XaUMODtWUCVrVt4g//Ube8o61/sOAYNSjQY4x6Gz8GQhqvj1BusWGxqdXUI30lM6C519biLDj2tbqd2lq/JWBEohpAGvVzaQnoJwodNQO8e+4LIURLnA4G2O32zhyHEH7PcaXWQ0/O8mWZgE+JdOYEyUd9d7qAe97by4XSaoLNk7jmZ3cxKuwEHF+vXl03Bqrrv01B6q0xsG7LPQy734TSbNi4DLb+Xe0dn7ykU1rHxRorudmwidvO7IIVB+q+YQyEAVdB7ECw20Cx1d7a1Vt7jbqvphrO/6gWvavIg+Pr1E0TkQg9x8Hw+dB/jqPTQuFF1dSDvLx7SKOZATUWOH8Ajq2HY1+pmRYXi+ildp4YeBUkTZeCgH5Ge1309qwYIYRoiedUSRLCz9VlBnjmMoGC8tplApIZ4BM8PfjUGRRF4a3tp1j+1RFsdoV+Xbvw2i/G0K9rKNAD+kxv+UEGXwuT74UfP4HUl9UTyu/+Ad+9BQOvVteRJ05pXzs5uw3dsa8Yn/4C3fbv53qTFaoBdJA0FUYsgME/UZc9OMtaCTk/QtYeOLdXvc0/AUVn1O3HjyG6P0xaAiNvcSwR8Jhq6ooCZefVVpEXjqj7wrpDWA+1lV9oNzA0fhU3Wl/GJP1BhlpOY//kY/Tnf4Tco2C/+LVWpxYDHHiVGgToOlhaAvoxR2aABAOEED7OJcGAN998k8rKSu69915XPJ0QXinKwwsI5pdpmQGyhtIXRHp48KmjlVZZefCjH/jqxxwAfjoynuU/G05IWzoHGANg1C0w8mY4vU0NChz7Go6uVbduw2H0Ihg+r3VLCKpKYN+7sOs1jEVniK/dfcSewCbzDJb89iEI79n68YKa5ZAwXt00lUWQnQbHU2DvO2qBvP8+AJv+TFifW4hhKBHBoW17vvaoLILcI2o2w4XDtdshqCxo5k466NIVwuIhNF79d8k5OP8j0SVZrNLO6X646C4B4WpwZeDV0P9K6BLbeT+T8CpaJpy3d9IQQoiWuCQY8Mc//pGCggIJBgjRDI8vIChXSnyKo2ZAG4JPm46c5+O9WeBFZWQOZBWTUVCByaDjsWuHsDA5sf1XvHU6SJqmbnnH1S4Faasg5wB89XtY90f1RHPUz6HfFWoxv8bkn4Tdb6iBAEsZAEpgBCdDkzFOv4+r3i0kVGdkSVsDAU0JioA+l6vb5X+Avf9Wq+YXZ5D440t8G2Bkh2UWXIhVr5R3lvI8dZ3+qW8g/RsoPNX4cTo9RPWFroPU4oil2eoJf2m2uiyi7Ly6sa/BXTOJ40dbLyYkTyO671g1YNOKgorCv0hmgBDCX8gyASE8hCcXELTbFceHo5gukhngCxzzrQ3LBB7//BAZBRUdPaROFx8eyMs/H8PoXpEd/+Ax/eHa52DmY3DgQ/XEPucHteL/4c8hpCuMXKB2Iogboqa9n9qqnnwf+xpHZCVmICT/hpohP+NgyhaSe44EtlBaXYPVZsdk0Hf82AECQtUlAhPugiNfkLv+GWKLDzCjYh28sg56T4WoJLWifkDoJVtY3f7AMHX5gim46RPt6lI4s0M98T/1jVrT4FJhPdXfU9fB0LX2NmYgmAIbHmu3q/UQSrKgJBtKz0FpDnSJg7hhEDeUX760l/S8cj4YnEx0n8aLJgqh0d6HJTNACOHrJBgghIfQPnSUVnXyh/42KKmyUmNXT1Yipe+yT9CueJW0cr7V2OxkFVUC8OBVAwkxe8fbSIBRz5VDu3V+QbDgKJj4a3XLOaBmCvywGsovwI4X1S1+NNis9U+C+81WK9b3nameRFvVDKHwoLq/t+JKa+cH4wxGGHoD754byrZNX/JE1y0MK9mqLoc4vc35x9Eb1QCBFhwIqL0tz1XrFdhr6h8fN0wt1NdnOiRMVLMWnH4uvbosoEtX9XfbiKgQM+l55X5VI0O0jd2uODL0pEaOEMLXecenOCH8QHiQCZ1OvWBYVGElNtRzrsBrSwRCA4wEGKWqti9o63zLLq7CZlcwG/TcPa0ver2kWTep23C4ajjMXqauy097T80COFebxm4KVjsRTLxbzSxohEGvIyzQSElVDUUVLggG1CqqsLBXGcBXQ65m2PgAOLEBqorUq/oNthK13oF2q9R2M6gsaHqdf2SSuryiz3ToPa3T1+trQaB8CQaIFpRW1WCrDX5HSGtBIYSPk2CAEB5C/dBvorjSSlGFxbOCAVrxQLlK4jMMeh3hQSaKKlo3384WqlkBPSKDJBDgLIMJBs1Vt/I8OPSZGoUZPg+CWl6yEBFspqSqhuJK153IaldGI4PN6vKACXc6d0dFAWsFVBWrgYGq4togQbG6mYKg92Vq6z4X0lqiSmaAaInWVrCLBL+FEH5AggFCeJCoEDPFlVaPKyKotRWUYkq+JSrYTFGFtX7/9RacLVRrBfSMDOqsYfm2kBgYf0er7hIRbCKjAEe7P1do85ppnQ7MIeoWFt/y8S4imQHCWdrroSyJE0L4A89ZlNyBXnnlFZKSkggMDGTs2LFs29b8OsdvvvmGsWPHEhgYSJ8+fXjttdcaHPPxxx8zZMgQAgICGDJkCJ9++mlnDV/4MS0l0dOKCOY5MgM8J1tBtF9b5puWGSDBANeJcEMbSC3w4CsnRNHt6J4h/IsWDIiS4oFCCD/gdGZARkZGm5/Ebre3+b6ttXr1au6//35eeeUVpkyZwuuvv87VV1/NoUOH6NWrYVriqVOnmDt3LnfeeSfvvvsu3377LUuWLCE2NpYbb7wRgNTUVBYsWMCTTz7JDTfcwKeffsr8+fPZvn07EydOdNnPJnyfdhWuyMM+sGofjqIlM8Cn1LUXdP4ksy4YENwpYxINRdQWEXTl64J20hzhIydE2mtra7JghH8qdGQG+MbcF0KI5jgdDEhKSmrzkyiK0v5+zk569tlnWbx4MXfcoaZhrlixgnXr1vHqq6+yfPnyBse/9tpr9OrVixUrVgAwePBgvv/+e/7v//7PEQxYsWIFs2fP5uGHHwbg4Ycf5ptvvmHFihWsWrXKJT+X8A91V2o9bZmA1AzwRW05QZJlAq6nvS4UV7pwmUC5b7VWi+oiwQDhHK1mgGQGCCH8gdPBAEVROnMcHcJisbBnzx7+8Ic/1Ns/Z84cduzY0eh9UlNTmTNnTr19V155JW+99RZWqxWTyURqaioPPPBAg2O0AEJjqqurqa6udnxdUlICgNVqxWr1rBO9i2lj8+Qx+rLwQPVPMr+0yqP+D3JLqgB1fJ40rsbIHHZeWKBaHKs1800LBnQLNcvvuBNdPI9DA9T/p4Kyapf8zi01dsotNgC6mHQ+8f8cZlZXRea76HcovPe1OK+09v0uyPPf70Tn89Z5LISzc9bpYMDmzZvbPBhXycvLw2azERcXV29/XFwcOTk5jd4nJyen0eNramrIy8uje/fuTR7T1GMCLF++nGXLljXYv379eoKDPT+9NiUlxd1D8EsXzuoAAz8cTWet7YS7h+NwLEMP6Mk8foi1RQfdPRynyBxu2YUsdb79ePwUa5WTLR5vUyC7yADoOLp3Bzk/dvoQ/V5KSgrnstX/p0Mnz7B27alOf85iC4ARHQrbN6fgC00j8qsAjOSXVvHll2txUbKiwPteiw+cUN/vcs+ms3Zty6+Lwj942zwWoqKiwqnjnA4GTJ8+vc2DcbVLlyS0tEyhseMv3d/ax3z44YdZunSp4+uSkhISEhKYM2cOYWFhLf8QbmK1WklJSWH27NmYTL5ROMqbFO3O5MvMw4RGxzF37mh3D8fh5ZM7oKSMmVMmcFm/aHcPp1kyh51XvucsX2QcIjiyK3Pnjmnx+LOFldh3bsNk0LHgp1dLa8FOdPE8thzM5dPTPxIcEcvcuWM7/bmPnS+FPalEBJu59poZnf58rlBeXcMT+zZhVXRcfsUcQgKkmVJn89bX4jXv7oPcXJJHD2fuuJ7uHo5wM2+dx0JoWekt8al3w5iYGAwGQ4Mr9hcuXGhwZV/TrVu3Ro83Go1ER0c3e0xTjwkQEBBAQEDDyusmk8krXky8ZZy+JjZMXYddXFXjUb//gtoaBl3DgzxqXM2ROdyy6NDWzbecUvWNpWdkMAEBsp7WFUwmE9GhgQCUuOh1odSiBsQjQ8w+8zcUbjQSYNRTXWOn1KIQ0cU3fi5v4G2vxUW1tTliQr3n/U50Pm+bx0I4O199qrWg2Wxm7NixDVJ5UlJSmDx5cqP3mTRpUoPj169fz7hx4xy/xKaOaeoxhWgrTywgaLcrjsriMdJa0Kc4ugk4WVRNige6R3iQa9viaV0LfKV4IKjZfdp8lyKCojmO1oLSTUAI4Qeczgx45513nHtAo5GIiAgGDx7crg4EbbV06VIWLlzIuHHjmDRpEm+88QYZGRncfffdgJq+n5WV5fh57r77bl566SWWLl3KnXfeSWpqKm+99Va9LgH33Xcf06ZN4+mnn+a6665jzZo1bNiwge3bt7v85xO+zRNbCxZXWrHZa68U+tDJgWh9N4G6toISDHClSK2bgIuChAXl1nrP6yuiQsxkF1c5qsUL0Zi6YIBvzX8hhGiM08GA2267rdXtAQcPHsyyZcscLfpcYcGCBeTn5/PEE0+QnZ3NsGHDWLt2LYmJiQBkZ2eTkZHhOD4pKYm1a9fywAMP8PLLLxMfH88LL7xQb8yTJ0/mgw8+4NFHH+Wxxx6jb9++rF69mokTJ7rs5xL+QTs5K6ywurQlZ3Pyy9WuGGGBRsxGn0om8nvayV5JVQ01NjtGQ/P/v3XBAM8vgupLImpfF0qra7Da7Jha+H9qLy0DIcLHgn+OzIAyCQaIxlltdkqqagAJfgsh/EOraga0tr3goUOHmD9/Pk899RQPPfRQq+7bHkuWLGHJkiWNfm/lypUN9k2fPp29e/c2+5jz5s1j3rx5HTE8IZqkLROw2RVKqmoID3L/lYn82g/O0bJEwOeEB5nQ6UBR1HWyLS0DkWUC7hEWWPdWXVJp7fS/RS0zydfSpB3LYiQzQDShqDb7RqfzvWCYEEI0xunLC3a73anNarVy/vx51q1bx0033YSiKDz66KP88MMPnflzCOETAk0GgkxqT3FPWSqgpUxG+9iJgQCjQe8IODlTN0CWCbiH0aAntDYg4Ip6ItpzRPjYMgHtSm++1AwQTXBkxQSZMEi3FCGEH+jwXEODwUBsbCyzZ89m9erV/OEPf8Bms/Haa6919FMJ4ZPqrl55RhHBPCmm5NMuXprSnBqbnZySKkCWCbiD9v9UXNn5J7K+WEAQ6gKazhbMFP5HC35HyvudEMJPdPoC4EceeQSz2cw333zT2U8lhE+o6yjgGR9YC2SZgE/T6ga0VEQwu7gKm13BbNATK3PB5bTXhSIXZgb4WgFB7QRPMgNEU7RAUZSPBcKEEKIpnR4MCAkJYdCgQZw9e7azn0oIn+BpHQW0AoKyTMA31WUGND/ftCUCPSKD0Ev6rMtpyzlcEgwo980CgpIZIFqSL5kBQgg/45LS4IGBgVRVVbniqYTweo7MgHLPWCaQL8sEfFqkk0XVpHigezkbtOkIhT66TECb68620hT+RzIDhBD+xiXBgKysLCIjI13xVEJ4PY/LDCirzQzoIh+OfFGUk1dLpa2ge2lBwuLKzg0S2u2K4zkifazPupYZUOAhr63C82hzQzIDhBD+olWtBdti9+7dZGVlccUVV3T2UwnhE7R1uvvPFrP2QLabRwOZBepJYHSIrBP3RXU1Kpo/yZROAu4V4aJlAiVVVuyK9py+dUKkneAVVVipsdkxGlxyPUR4EUdmgI8FwoQQoimdGgw4cuQICxcuRKfT8bOf/awzn0oIn6EV6vvmWC7fHMt182jqxIT61omBUGnpsC1nBsgyAXcKd9EyAS0o1CXAiNnoWyfLEUEmdDpQFCiqtBIjhTDFJQocxTPl/U4I4R+cDgY88cQTTh1XU1NDYWEhaWlppKamYrfbGTx4MLfddltbxyiEX7l6eDdST+Z7VCrroG6hDIwLdfcwRCeIdDJ1WpYJuFeki5YJOPqs+1gnAQCjQU94kImiCisF5RYJBogGtKCoLIsTQvgLp4MBjz/+ODqd8xWkFUXNMxw+fDifffYZgYGBrR+dEH6oa2ggry0c6+5hCD9RV6Oi6ZPMGpudnBK1CGyCZAa4hataC2onQ756ZTQqxOwIBghxqQIfn/9CCHEpp4MBvXr1cioYYDAYiIiIYPDgwVx77bXMmzcPg8HQrkEKIYToHNra2OZOjrKLq7DZFcxGvVxNdZPw2vX7RZWuWSbgi5kBoC6LSadcggGiUQXSPUcI4WecDgacPn26zU9SVlaG3W4nLCyszY8hhBCi42lXwIormy6q5lgiEBGEXu98hpjoOI7MgE5uOap1MfHVk6EoaS8omlBpsVFptQHSTUAI4T9cUh0oKSmJqKgoVzyVEEKIVggPqrsC3NR6dK14YA9ZIuA2WtCmtLoGq83eac+j1Qzw1TRpCQaIpmhz36jXERrQ6c22hBDCI7isVLBWQ0AIIYTn0IqqQdOV6qV4oPuFBdadnJR0YhFBn18mIMEA0QRHvYAQc6tqZAkhhDeT0KcQQvi5qBAzxZVWx4ngpeqCAZIZ4C5Gg57QQCOlVTUUVVodLUg7WpFkBjicL6ni7e2nHKnjonXsdjtnTuv57r+H0es9v01lTrFaJDXKR+e+EEI0RoIBQgjh57SrwE2dIGnLBCQY4F4RwSY1GNCJbUe1OeDrmQFNZcFc7G9fH+XjvWc7e0g+Ts+285nuHkSryHIoIYQ/kWCAEEL4Oe1KWGGTwQBZJuAJIoPNZBZUdmp7Qe2xfTUzQCsMl1/WfDCgwlLDVz9mA/DLSYmE++jvozPZbTaOnzhB/3790HtJVymjXsdPR8a7exhCCOEyEgwQQgg/F6EFAxo5yayx2ckpUdNnE+SKmVtptR06MxhQ6OPdBKKdzAxYf/A8FRYbidHBPP7TobKGvA2sVitrq48xd1Y/TCbfzDQRQghv5/mLuIQQQnSqqJCmCwhmF1dhsyuYjXpiOmmdunBOXdCmc5YJKIri8wUEtYyH/HJLs4WNteUB14/qIYEAIYQQPkuCAUII4ecimymqlqnVC4gIQq+XkyJ3iqjNDGiqBWR7VVptWGrUtoW+ukwguov6c1lq7FRYGi8MeKGkim9P5AHwszE9XDY2IYQQwtWcXiYwc+bMNj9JcXFxm+8rhBCic2knfo0VptPqBUhRLfeLDO7cZQJaVoDZoCfY7B1rvFsryGQgwKinusZOQbmFkEb6ya9JO4ddgbGJkSRGh7hhlEIIIYRrOB0M2LJlCzqdrtm0uuZImp0QQngmLRjQWGaAFA/0HFoRu6JOygwovKiTgK++Z+t0OqJDzJwrrqKg3EJCVMN5rS0RuGG0ZAUIIYTwbU4HAxYtWuSzHw6EEMKf1bVba3iSKW0FPUeEo4Bg59QM0GoR+OoSAU3kRcGASx3OLuFITilmg55rR3R3w+iEEEII13E6GLBy5cpOHIYQQgh30dLPGytMV5cZIMEAd4sMcc0yAe15fFVUMzUyPt2XBcDMQV0dBRuFEEIIXyUFBIUQws9pBQSLK63Y7PWXgmXJMgGPER6kLRPonMyAIj/JDGgqGGCzK3xWGwy4QQoHCiGE8AMSDBBCCD+npZ8rSv1K9VabnexiNRiQIJkBbhfR2QUEy7W2gr4dDHDUyLgkE+bbE3lcKK0mItjEjIFd3TE0IYQQwqUkGCCEEH7OaNATFqiuGrv4amlOcRV2BcxGPTFdAtw1PFFLC9qUVtVQY7N3+OPX1Qzw7WUC0VpmQFn9YIC2ROAnI+IxG+XjkRBCCN/nU+92hYWFLFy4kPDwcMLDw1m4cCFFRUXN3kdRFB5//HHi4+MJCgri8ssv5+DBg/WOufzyy9HpdPW2m2++uRN/EiGEcK26IoJ1J0iZWvHAiCD0eikg627hQXUn6cWd0FHAnwoIQv3MgPLqGr7+MQeQJQJCCCH8h08FA2699VbS0tL4+uuv+frrr0lLS2PhwoXN3udvf/sbzz77LC+99BLfffcd3bp1Y/bs2ZSWltY77s477yQ7O9uxvf766535owghhEtpqeGFF2UGaMUDe8gSAY9gNOgJrc3g6Iz2gnUFBH07GKBlBlw817/+MYdKq42kmBBGJ0S4aWT/n707j4+ivv84/trdbDYHOQkkhPsSlEM5lEs8QFDRVm1VPOrRKtai9aC/2lq19Wil2qp41LNWar2o9W5RiYh4gIIIiFxyhyPhyH3vZnd+f0x2k5AENpvd7G7yfj4e80iymd35bhh2Zj7z+X4+IiIi7cvvbgKRbuPGjXzwwQd8+eWXjBs3DoDnnnuOCRMmsHnzZoYMGdLkOYZhMG/ePO644w5+9KMfAfDPf/6TzMxMXnnlFX7+85/71k1ISCArK6t93oyISDtrLjNgj4oHRpzUBDtl1bUhaS9Y3EmmCaQ1U0DQO0XgglE91UZZREQ6jQ6TGbB8+XJSUlJ8gQCA8ePHk5KSwrJly5p9zo4dO8jPz2f69Om+xxwOB6eeemqT57z88stkZGQwbNgw/u///q9J5oCISDTzFVWrqL/jvMc7TUCZAREj1dtRIARFBL2BoI5eQLDrYdME8kuq+WLbIcAMBoiIiHQWHSYzID8/n+7dm1b/7d69O/n5+S0+ByAzM7PR45mZmezatcv38+WXX07//v3Jysriu+++4/bbb2ft2rXk5OS0OJ6amhpqamp8P5eWlgLgcrlwuUJTCToYvGOL5DGKHIn24cCkxNkAKCiv9v3tdheawYAeybH6e7azlvbjlHjzsF1QVh30fxNvN4GkWEuH/vdOijXv/BdXuqiqruGNVbkYBoztm0pWkr1Dv/f2pM9i6Qi0H0u08nefjfhgwN13380999xzxHVWrlwJ0Gxqn2EYR035O/z3hz9n1qxZvu+HDx/O4MGDGTt2LN988w2jR49u9jXnzp3b7LgXLVpEQkLkp9weKdAhEg20D7fOgb0WwMa6zdtZ6N4KwNZ9NsBC7sbVLNyzOqzj66wO348ri6yAleXfrMWRtyZo23F7oLzGPCX4+oulbOzAMwXcBliwYWDhP+99wEsbzP18oK2AhQsXhnt4HY4+i6Uj0H4s0aaystKv9SI+GHDjjTcetXJ/v379+Pbbb9m/f3+T3x08eLDJnX8vbw2A/Px8evTo4Xv8wIEDLT4HYPTo0djtdrZs2dJiMOD2229nzpw5vp9LS0vp3bs306dPJzk5+YjvJ5xcLhc5OTlMmzYNu70Dnw1Kh6V9ODBlX+/hv7kbSEzPZMaMUbjcHm798iMALpoxle5Jai3Ynlraj1e4N/JNwW569B3MjDMGBW17B8tq4KulWCzwox+cja2Dd4+4d+0SiqtcxPcfRd6qdcTGWLntktNJjtdnRrDos1g6Au3HEq28WelHE/HBgIyMDDIyMo663oQJEygpKWHFihWcdNJJAHz11VeUlJQwceLEZp/jTf3Pyclh1KhRADidTpYuXcoDDzzQ4rbWr1+Py+VqFEA4nMPhwOFoevJst9uj4sMkWsYp0hLtw62TkRQHQEl1LXa7nfyySjwGxMZY6ZGaqNaCYXL4fpzexTyulNW4g7p/l7uqAUiNtxPn6Ng1AwDSu8RSXOVi/nJzSuAZx3ana3LkZ+1FI30WS0eg/Viijb/7a4cpIHjsscdy1llnMWvWLL788ku+/PJLZs2axbnnntuok8DQoUN56623AHN6wC233ML999/PW2+9xXfffcfVV19NQkICl112GQDbtm3j3nvv5euvv2bnzp0sXLiQiy66iFGjRjFp0qSwvFcRkWBLO6y14G5v8cDUeAUCIoi3uF+wWwt6/93TOnjxQK/0uvf53V7zzsmPRvUK53BERETCIuIzA1rj5Zdf5qabbvJ1B/jhD3/IE0880WidzZs3U1JS4vv5tttuo6qqitmzZ1NUVMS4ceNYtGgRSUlJAMTGxrJ48WIeffRRysvL6d27N+eccw5/+MMfsNls7ffmRERC6PDWgt62gj3VSSCipNalsQe7tWBRXXeC1A7eVtDLu797vz91SLcwjkZERCQ8OlQwID09nZdeeumI6xiG0ehni8XC3Xffzd13393s+r1792bp0qXBGqKISERqeMfZ7TF8wYBeaUqdjiTei/Vgtxb0Bhc6TWZAg2DAD0b2wG7rMImSIiIiftPRT0REfBeZhgElVS72eKcJKDMgoviCAVWhygzofMGAH43WFAEREemcFAwQERHsNivJcWayWFGls0FmgIIBkcSXwRHkzIAiX2ZA55omMKBbIiN7pYR5NCIiIuHRoaYJiIhI4NISYymtrqWowsleTROISN6aAWXVtdS6PcQEKb3dV0AwsXNkBvzg+GyWfn+QaycPwGJRgUwREemcFAwQERHAnC++q6CSA2U15JWYwYDeygyIKCnx9XfuS6pcdO3StIVtILzTBDpLzYDM5Dj+dc24cA9DREQkrDRNQEREgPoU8Y15pXgMcMRY6ZYUnItNCY4Ym5WkuukcwWwvWNzJpgmIiIiIggEiIlLHmyL+7R6z/WrPtHilUEegUHQU8NYM6CwFBEVERETBABERqZNedyG4bq8ZDFC9gMiUGu8tIhi8jgK+aQKJygwQERHpLBQMEBERoD4zoLCumJw6CUSmYGcGeDxGg2kCygwQERHpLBQMEBERoOmFoIIBkcnXXjBINQPKqmvxGN7XVmaAiIhIZ6FggIiIAJB+WIq4pglEJm97wZIgTRPw1gtIjLXhiLEF5TVFREQk8ikYICIiQNPiccoMiEzeu/dFQZomoOKBIiIinZOCASIiAkB6ooIB0SClLjMgWNMEilU8UEREpFNSMEBERIDGNQMcMVa6dXGEcTTSEu+/U7C6CXgLRqp4oIiISOeiYICIiACNi8f1TIvHYrGEcTTSEu+/U0mQMgM0TUBERKRzUjBAREQAsNusJMXFACoeGMnqawYEJzPAO00gXZ0EREREOhUFA0RExMebKq56AZErJd47TUCZASIiIhI4BQNERMQnLVHBgEiXVncHv6y6llq3p82v5ysgqMwAERGRTkXBABER8RmYkQjAsOyUMI9EWuLtJgDBqRvgzQxIS1RmgIiISGcSE+4BiIhI5LjnvGFcNq4PY/qmhXso0oIYm5UkRwxlNbUUV7no2sauD95uApomICIi0rkoM0BERHyS4uyM7ZeuTgIRLqUupT8YdQM0TUBERKRzUjBAREQkyngLPZZUtb2jgG+agDIDREREOhUFA0RERKKMr71gRdsyA6qcbmpqzSKEqhkgIiLSuSgYICIiEmW8RQSL21hA0JsVYLdZSIy1tXlcIiIiEj0UDBAREYkyvmkClW2bJuANBqQmxKpOhIiISCejYICIiEiU8U4TaHNmQIWKB4qIiHRWCgaIiIhEGe80gaI2dhNomBkgIiIinUtMuAcgIiIireO9eD9YVk1BeU3Ar7OvuAqAdAUDREREOh0FA0RERKKMN63/y+2FjPnjR21/vURNExAREelsOtQ0gaKiIq644gpSUlJISUnhiiuuoLi4+IjPefPNNznzzDPJyMjAYrGwZs2aJuvU1NTwy1/+koyMDBITE/nhD3/Inj17QvMmREREjuKE3qn0TI0PymvF222cPqR7UF5LREREokeHygy47LLL2LNnDx988AEA1113HVdccQXvvfdei8+pqKhg0qRJXHTRRcyaNavZdW655Rbee+89XnvtNbp27cqvfvUrzj33XFatWoXNplZMIiLSvrp2cfDFb6eEexgiIiISxTpMMGDjxo188MEHfPnll4wbNw6A5557jgkTJrB582aGDBnS7POuuOIKAHbu3Nns70tKSnj++ef517/+xRlnnAHASy+9RO/evfnoo48488wzg/9mREREREREREKowwQDli9fTkpKii8QADB+/HhSUlJYtmxZi8GAo1m1ahUul4vp06f7HsvOzmb48OEsW7asxWBATU0NNTX1RZ1KS0sBcLlcuFxtq/4cSt6xRfIYRY5E+7B0BNqPJdppH5aOQPuxRCt/99kOEwzIz8+ne/emcx67d+9Ofn5+m143NjaWtLS0Ro9nZmYe8XXnzp3LPffc0+TxRYsWkZCQEPB42ktOTk64hyDSJtqHpSPQfizRTvuwdATajyXaVFZW+rVexAcD7r777mYvqhtauXIlABaLpcnvDMNo9vG2Otrr3n777cyZM8f3c2lpKb1792b69OkkJycHfTzB4nK5yMnJYdq0adjtqi4t0Uf7sHQE2o8l2mkflo5A+7FEK29W+tFEfDDgxhtv5JJLLjniOv369ePbb79l//79TX538OBBMjMzA95+VlYWTqeToqKiRtkBBw4cYOLEiS0+z+Fw4HA4mjxut9uj4sMkWsYp0hLtw9IRaD+WaKd9WDoC7ccSbfzdXyM+GJCRkUFGRsZR15swYQIlJSWsWLGCk046CYCvvvqKkpKSI160H82YMWOw2+3k5ORw8cUXA5CXl8d3333Hgw8+GPDrioiIiIiIiISLNdwDCJZjjz2Ws846i1mzZvHll1/y5ZdfMmvWLM4999xGxQOHDh3KW2+95fu5sLCQNWvWsGHDBgA2b97MmjVrfPUAUlJSuOaaa/jVr37F4sWLWb16NT/5yU8YMWKEr7uAiIiIiIiISDTpMMEAgJdffpkRI0Ywffp0pk+fzsiRI/nXv/7VaJ3NmzdTUlLi+/ndd99l1KhRnHPOOQBccskljBo1iqefftq3ziOPPML555/PxRdfzKRJk0hISOC9997DZrO1zxsTERERERERCaKInybQGunp6bz00ktHXMcwjEY/X3311Vx99dVHfE5cXByPP/44jz/+eFuHKCIiIiIiIhJ2HSozQERERERERESOTsEAERERERERkU6mQ00TiGTe6Qn+9nwMF5fLRWVlJaWlpWqhIlFJ+7B0BNqPJdppH5aOQPuxRCvvNefhU+QPp2BAOykrKwOgd+/eYR6JiIiIiIiIdHRlZWWkpKS0+HuLcbRwgQSFx+Nh3759JCUlYbFYwj2cFpWWltK7d292795NcnJyuIcj0mrah6Uj0H4s0U77sHQE2o8lWhmGQVlZGdnZ2VitLVcGUGZAO7FarfTq1Svcw/BbcnKyPvQkqmkflo5A+7FEO+3D0hFoP5ZodKSMAC8VEBQRERERERHpZBQMEBEREREREelkFAyQRhwOB3/4wx9wOBzhHopIQLQPS0eg/ViinfZh6Qi0H0tHpwKCIiIiIiIiIp2MMgNEREREREREOhkFA0REREREREQ6GQUDRERERERERDoZBQNEREREREREOhkFA8TnySefpH///sTFxTFmzBg+++yzcA9JpEVz587lxBNPJCkpie7du3P++eezefPmRusYhsHdd99NdnY28fHxnHbaaaxfvz5MIxY5srlz52KxWLjlllt8j2kflki3d+9efvKTn9C1a1cSEhI44YQTWLVqle/32ocl0tXW1nLnnXfSv39/4uPjGTBgAPfeey8ej8e3jvZj6agUDBAAFixYwC233MIdd9zB6tWrmTx5MmeffTa5ubnhHppIs5YuXcoNN9zAl19+SU5ODrW1tUyfPp2KigrfOg8++CAPP/wwTzzxBCtXriQrK4tp06ZRVlYWxpGLNLVy5UqeffZZRo4c2ehx7cMSyYqKipg0aRJ2u53333+fDRs28NBDD5GamupbR/uwRLoHHniAp59+mieeeIKNGzfy4IMP8pe//IXHH3/ct472Y+mwDBHDME466STj+uuvb/TY0KFDjd/+9rdhGpFI6xw4cMAAjKVLlxqGYRgej8fIysoy/vznP/vWqa6uNlJSUoynn346XMMUaaKsrMwYPHiwkZOTY5x66qnGzTffbBiG9mGJfL/5zW+Mk08+ucXfax+WaHDOOecYP/vZzxo99qMf/cj4yU9+YhiG9mPp2JQZIDidTlatWsX06dMbPT59+nSWLVsWplGJtE5JSQkA6enpAOzYsYP8/PxG+7XD4eDUU0/Vfi0R5YYbbuCcc87hjDPOaPS49mGJdO+++y5jx47loosuonv37owaNYrnnnvO93vtwxINTj75ZBYvXsz3338PwNq1a/n888+ZMWMGoP1YOraYcA9Awu/QoUO43W4yMzMbPZ6ZmUl+fn6YRiXiP8MwmDNnDieffDLDhw8H8O27ze3Xu3btavcxijTntdde45tvvmHlypVNfqd9WCLd9u3beeqpp5gzZw6/+93vWLFiBTfddBMOh4Mrr7xS+7BEhd/85jeUlJQwdOhQbDYbbrebP/3pT1x66aWAPoulY1MwQHwsFkujnw3DaPKYSCS68cYb+fbbb/n888+b/E77tUSq3bt3c/PNN7No0SLi4uJaXE/7sEQqj8fD2LFjuf/++wEYNWoU69ev56mnnuLKK6/0rad9WCLZggULeOmll3jllVcYNmwYa9as4ZZbbiE7O5urrrrKt572Y+mINE1AyMjIwGazNckCOHDgQJMoqEik+eUvf8m7777LkiVL6NWrl+/xrKwsAO3XErFWrVrFgQMHGDNmDDExMcTExLB06VIee+wxYmJifPup9mGJVD169OC4445r9Nixxx7rKz6sz2GJBr/+9a/57W9/yyWXXMKIESO44ooruPXWW5k7dy6g/Vg6NgUDhNjYWMaMGUNOTk6jx3Nycpg4cWKYRiVyZIZhcOONN/Lmm2/y8ccf079//0a/79+/P1lZWY32a6fTydKlS7VfS0SYOnUq69atY82aNb5l7NixXH755axZs4YBAwZoH5aINmnSpCYtXb///nv69u0L6HNYokNlZSVWa+NLIpvN5mstqP1YOjJNExAA5syZwxVXXMHYsWOZMGECzz77LLm5uVx//fXhHppIs2644QZeeeUV3nnnHZKSknwR+5SUFOLj43392u+//34GDx7M4MGDuf/++0lISOCyyy4L8+hFICkpyVfjwisxMZGuXbv6Htc+LJHs1ltvZeLEidx///1cfPHFrFixgmeffZZnn30WQJ/DEhV+8IMf8Kc//Yk+ffowbNgwVq9ezcMPP8zPfvYzQPuxdHBh7GQgEeZvf/ub0bdvXyM2NtYYPXq0r0WbSCQCml1eeOEF3zoej8f4wx/+YGRlZRkOh8M45ZRTjHXr1oVv0CJH0bC1oGFoH5bI99577xnDhw83HA6HMXToUOPZZ59t9HvtwxLpSktLjZtvvtno06ePERcXZwwYMMC44447jJqaGt862o+lo7IYhmGEMxghIiIiIiIiIu1LNQNEREREREREOhkFA0REREREREQ6GQUDRERERERERDoZBQNEREREREREOhkFA0REREREREQ6GQUDRERERERERDoZBQNEREREREREOhkFA0REREREREQ6GQUDRERERERERDoZBQNEREREREREOhkFA0REREREREQ6GQUDRERERERERDoZBQNEREREREREOhkFA0REREREREQ6GQUDRERERERERDoZBQNEREREREREOhkFA0REREREREQ6GQUDRERERERERDoZBQNEREREREREOhkFA0REREREREQ6GQUDRERERERERDoZBQNEREREREREOpmYcA+gs/B4POzbt4+kpCQsFku4hyMiIiIiIiIdkGEYlJWVkZ2djdXa8v1/BQPayb59++jdu3e4hyEiIiIiIiKdwO7du+nVq1eLv1cwoJ0kJSUB5j9IcnJymEfTMpfLxaJFi5g+fTp2uz3cwxFpNe3D0hFoP5Zop31YOgLtxxKtSktL6d27t+8atCUKBrQT79SA5OTkiA8GJCQkkJycrA89iUrah6Uj0H4s0U77sHQE2o8l2h1teroKCIqIiIiIiIh0MgoGiIiIiIiIiHQyCgaIiIiIiIiIdDIKBoiIiIiIiIh0MgoGiIiIiIiIiHQyCgaIiIiIiIiIdDIKBoiIiIiIiIh0MgoGiIiIiIiIiHQyCgaIiIiIiIiIdDIKBoiIiIiIiIh0MjHhHoCIRLZf/Xst3+0t4Z0bJxFnt4V7OCIiIkGXW1DJj55axqHymiOuFxtj5S8XjuS8E3oGvK2/f7adue9vwu0xjrhen/QE3vvlyaTE2wPajsvt4cKnlrF2T0lAz2+tOLuVhy8+gRkjerTL9kSk7ZQZICItMgyD977dx+b9ZWw9UB7u4YiIiITEl9sLjhoIAHDWesjZsL9N23r/u/yjBgIAcgsrWdeGC/ndhZXtFggAqHZ5WLzxQLttT0TaTpkBItKiSqcbZ60HgMIKZ5hHIyIiEhqFleYx7pyRPbj3h8OaXWfhd/nc9fZ3FFW27XhYVHc8ffaKMYzpm9bsOte/tIqVO4t84wpoO3XP7ZUWzzs3TAr4dfzx9pp93PffDRS38W8jIu1LwQARaVHDAEBbT35EREQilfcCvUdyHF27OJpdp3daPACFFa42bct7gd8vI7HFbXVPims0roC2UzfOjC6OFrcTLD1T6/42OlcQiSqaJiAiLWoYAGjLCYmIiEgk8wa/0xJjW1wnve53bTke1ro9lFSZF+lpCS1vKy3R3mhcgfCOM/0I7ylY0hLM8RZXti1QIiLtS8EAEWlRw5OQQh3gRUSkg/IGv4904ey9eC+sdGIYR5/z35ySKhfep6YmtFwYML1uW23JyvPepT9S0CFYfIESZQaIRBUFA0SkRcoMEBGRzsCXGXCEC+euXczfOWs9VDrdAW3He1xNibdjt7V8Gu7NUAhOZkBg3QhaI7Xu71ZS5fKrOKKIRAYFA0SkRQ3nRSraLyIiHZX3ott7wd+ceLsNR4y10fqt3455XD1a6n4w7rT7M/UhWLxZDoYBpVXKJBSJFgoGiEiLCivq2ywpGCAiIh2VP5kBFovFd5EeeDCgpm47R75b75uS0IZihb6pD+0wTcBus5LkiGm0XRGJfAoGiEiLGmUGtLF6soiISCRyuT2UVtcCR79j37BuQCBanRnQpm4C7ZcZAJBaNx2hSDWGRKKGggEi0qIitRYUEZEOzlsB32Ix5/IfSVsv0ov8LOrnqxnQhmKF3ovy9ugmAPXvSTWGRKKHggEi0qKGdz7aUsRIREQkUnkv0FPj7disliOu29bCfoV+tvvzpva3pVihP1Mfgik1CB0QRKR9KRggIi1qGN2vqfVQFeAJiYiISKRqTTp91zYW9ivyMxgQH2sjzh54scJat4eSqvbNDEivq4NQrGkCIlFDwQARadHhJzuBzpEUERGJVL5OAn5cNLe1sJ/3OOpP4CG9DXfai6v8n/oQLMoMEIk+CgaISLM8HsM33zCmLm1S8wBFRKSjaU06fXpdkbyG3XYC2ZY/Ff7bMiXBe7z2Z+pDsPhqBigzQCRqKBggIs0qrXbh9phFi/p0TQAU7RcRkY7H39R9qL9AD7TDTmumJKS3YUpCe3cSMLflnSagcwWRaKFggIg0y3si0cURQ2ZSXKPHREREOopAUvcDnTbXqsBDG6YkeAMI/mQgBEtqQtuKK4pI+1MwQESa5Wt/lGgPSr9jERGRSFQUQOp+IMfDapebirpCvP5sqy3HXm8AoV0zA1RAUCTqKBggIs3ynkikJzpIrTvAax6giIh0NIWV/l84N0zd99RNpfOX9yLZZrWQFBdz1PXT2pCFEI7MgDQVEBSJOgoGiEiz6u+U2Ns0b1FERCSSeYsBtqabgMcwa+u0bjv1hQqtfhT18xYrDCwzIBw1A8xtFVe6MIzWBUpEJDw6ZDDgySefpH///sTFxTFmzBg+++yzFtd98803mTZtGt26dSM5OZkJEybw4YcfNlpn/vz5WCyWJkt1dXWo34pI2DScQ5mmeYAiItJBFbUipT42xkqSw7yrX9DKY6Kvk0Cif63+gtFNwN9tBYN3moDT7aGybjqEiES2DhcMWLBgAbfccgt33HEHq1evZvLkyZx99tnk5uY2u/6nn37KtGnTWLhwIatWreL000/nBz/4AatXr260XnJyMnl5eY2WuLi49nhLImHRsP1RfYVgTRMQEZGOpTXt/iDwugG+ILuf20lvQ9p9a7cVDPF2G7Ex5qWFMglFosPRJyxFmYcffphrrrmGa6+9FoB58+bx4Ycf8tRTTzF37twm68+bN6/Rz/fffz/vvPMO7733HqNGjfI9brFYyMrKCunYRSJJwxRDZQaIiEhHVOV0U+Uy72KnteKOfW5hZauPia3pJODdDgTYTaCV2woGi8VCWoKd/aU1FFe66JXWbpsWkQB1qGCA0+lk1apV/Pa3v230+PTp01m2bJlfr+HxeCgrKyM9Pb3R4+Xl5fTt2xe3280JJ5zAfffd1yhYcLiamhpqamp8P5eWlgLgcrlwuSL37qp3bJE8RmkfBeXmNJiUOBvJjvpIf6TvG9qHpSPQfizRLlr24YOl5rHObrPgsBp+jTc13jx9PlRW1ar3d6isCoCU+Bi/ntcl1qwrUFTpxOl0YrEcvc6Al3cKQ1KstV3/DVLjzWDAgdJKXN0T2m27oRIt+7HI4fzdZztUMODQoUO43W4yMzMbPZ6ZmUl+fr5fr/HQQw9RUVHBxRdf7Hts6NChzJ8/nxEjRlBaWsqjjz7KpEmTWLt2LYMHD272debOncs999zT5PFFixaRkBD5H445OTnhHoKE2Y69NsDCjk3rcOYaQAyHyqpYuHBhuIfmF+3D0hFoP5ZoF+n78J4KgBjirR7ef/99v55TWWQFrCz/Zh2J+7/1e1trtpvPK9yXy8KFO4+6vstjjs3tMXjjvfdJaMVZ+8FS8xi+duUy8r7z/3lt5a4y3+Mny1ZS9n3HKSIY6fuxyOEqKyv9Wq9DBQO8Do+cGobhVzT11Vdf5e677+add96he/fuvsfHjx/P+PHjfT9PmjSJ0aNH8/jjj/PYY481+1q33347c+bM8f1cWlpK7969mT59OsnJya19S+3G5XKRk5PDtGnTsNvbr+iMRJ6HN38OVHLG5PEMzUri3tUf4/JYOP2MM4mPtYV7eC3SPiwdgfZjiXbRsg9/tvUQfPsN2elJzJgx0a/nfPvBZlYe3EX33gOYcdYQv7e1aMG3sD+fE48/lhkT+vr1nD+sXkyF082Jk06jb1f/bibV1HqoWf4RABfMmEZKfPv9/ReWrGHrhgP0O2YYM8b3abfthkq07Mcih/NmpR9NhwoGZGRkYLPZmmQBHDhwoEm2wOEWLFjANddcw+uvv84ZZ5xxxHWtVisnnngiW7ZsaXEdh8OBw+Fo8rjdbo+KD5NoGaeEjrf4T7fkBFIT44i1WXG6PZS7DJLbsTpxoLQPS0eg/ViiXaTvw2U1HgDSuzj8HmfXJLOAdHGVu1XvrajKTNvtlhTv9/PSEmOpcFZR6vT4/ZzCKnPqg81qIb1LvF9tDIPF+7cprWnd3ybSRfp+LHI4f/fXDtVNIDY2ljFjxjRJ5cnJyWHixJajva+++ipXX301r7zyCuecc85Rt2MYBmvWrKFHjx5tHrNIJHK5PZRW1wJm8SGLxUJqXcsgFREUEZGOomGxXH8FWuU/oG0F0LnAt50Ee7sGArzbBHUfEokWHSozAGDOnDlcccUVjB07lgkTJvDss8+Sm5vL9ddfD5jp+3v37uXFF18EzEDAlVdeyaOPPsr48eN9WQXx8fGkpKQAcM899zB+/HgGDx5MaWkpjz32GGvWrOFvf/tbeN6kSIh5T3AsFnzphemJsRwoq1G7IBER6TCKWtlWEBpW+W9lN4HKALYVQDefoor2byvoldaGdogi0v46XDBg5syZFBQUcO+995KXl8fw4cNZuHAhffuac7Py8vLIzc31rf/MM89QW1vLDTfcwA033OB7/KqrrmL+/PkAFBcXc91115Gfn09KSgqjRo3i008/5aSTTmrX9ybSXorq2hilxtux1d1VqD/AK9ovIiIdQ2FlG+7Wt+KC1zAM37HV3xaGgW4rkPcULKlqRSwSVTpcMABg9uzZzJ49u9nfeS/wvT755JOjvt4jjzzCI488EoSRiUSHwmb6E3tPXlqTqigiIhLJvBfo6Qn+X6AHcre+wunG6a6rT9CKi/T6bfkfiA8k2yFYNE1AJLp0qJoBIhIcvlTGhsEARftFRKSDKaioAcwCgv7qWndsLKuuxVV3gX803gv0OLuVhFj/78WlBxCIL/RlIIQvM0DTBESig4IBItJEYTPzDb3fF+sALyIiHUR9ZoD/F87J8Xa8dfn8vegtDPBuva8+QSuOvfUB/favfu+9iaDMAJHooGCAiDTR/DQB7wmJDvAiItIx1M+v9//C2Wa1tHpufCCdBKBB54KAugmEb5pAeU0tzlr/siZEJHwUDBCRJpo7aQkkVVFERCRSmUX9mga//ZHWyna7zQXZ/dpOmzID2j8YkBxXnzVRXKXzBZFIp2CAiDTRXPsjzQMUEZGOpKymllqPAbT+Lrqvyr+fhf28x87AtxNAZkAYggFWq8XXklhTBUQin4IBItJEc3cwAklVFBERiVTe41lCrI04u61Vz/UV1W1tzYBWZyDUzcGvcuGuC1wcTTi7CYAKDotEEwUDRKSJ5lIM0wNIVRQREYlUBW2YW9+1S+sC5IGm7qfWTUcwDCip8u9Oe2EYpwlA/ZhVcFgk8ikYICJNFDXTlsh7cK92eahyusMyLhERkWDxXsh7L+xbo7V3vwNN3bfbrCTHxfi9rSqnm2qXJ6BtBUuab1qhpgmIRDoFA0SkieZaIHVxxGC3mVWBVDdARESiXVuq7vuy5VpbQLAN2/Ln2OvNCoi1WUmMbd3Uh2BJa8V4RSS8FAwQkUaqnG6qXOad/4atliwWS4Novw7wIiIS3dpSdb+1x8P6zAD/Wxj6ttWKwENRg+1YLJZWbysY0hJUQFAkWigYICKNeO8q2G0WujhiGv3Od/LjZ/VkERGRSFXonRLXDpkB3pT5QAIPrSng25Zsh2BJVcFhkaihYICINNKw5/LhdxW8dzRURFBERKJd/fEu8Lv1/lzwuj2Gr5heINME0lpRwLct2Q7BopoBItFDwQARaeRIdxW8JxeqECwiItGuIMCifgBdW3GBXlrlwtsVMJBtpbci8FDUhvcULN5pAppSKBL5FAwQkUaOdFchVb2DRUSkg/Ae77oGUjOg7jn+dNjxBgyS4mKw21p/6l3fueDod9oLvdMRImGagIIBIhEv5uirNC83N5fly5ezadMmioqKqKysJCEhgbS0NIYOHcqECRPo06dPMMcqIu3gSO2PWjNvUUREJJIVtWF+fWKsjVibFafbQ0FFDb1iE1pct7Ciban73mkM/lxcR0RmQKIKCIpEi1YHA5YsWcJdd93F8uXLj7ru+PHjue+++5gyZUpAgxOR9nek9kf17YJ0gBcRkehW2Ib59RaLhbREO/tLayiqcNEr7QjbaWNRv7RWZOX53lNC6+sgBIv3/KG40onHY2C1hqergYgcXatylf785z9zxhlnsGzZMgzDwG63M2DAAMaOHcvJJ5/M2LFjGTBgAHa7HcMwWL58OdOmTWPu3LmhGr+IBNmRMgM0D1BERDqCWreHkqq6bgIB3kX3XaQf5ZhY1ObMAP/T7iMhM8A7TcBjQFl1bdjGISJH53cwYPHixfzud7/DMAwuvfRSli5dSllZGVu2bOGrr75i6dKlfPXVV2zZsoWysjKWLl3KJZdcgmEY3HnnnXz88cehfB8iEiRFR7ir0JpexyIiIpGqpMqFUVfULzU+sLvo/hb28wYLAs4MaMWxt61TEoIhNsZKYqwN0M0DkUjndzBg3rx5WCwWHn/8cV5++WUmT56M3d78h6fdbmfy5Mm88sorPProoxiGwSOPPBK0QYtI6PhOJLo4mvwuzZf6p2kCIiISvbzHupR4OzEBFPWD+gvuo12ke4MFXbsEmBlQd+wtq67F5fYceVttDDwES6qfWRMiEl5+f/qtWLGC7OxsbrjhhlZt4Je//CXZ2dl89dVXrR6ciLS/ooqWKxGnq5uAiIh0AN7jWCCdBLz8Td/3dgEI9AI9Od6Od9r9kbZlGEb9MTyMmQHQsIigzhdEIpnfwYDS0lKysrIC2khWVhZlZWUBPVdE2pcvnTGxuWkC5mNVLjfVriO3UhIREYlUvjvobbho9l7cFxxtmkBFDVDfFaC1bFZLfbu+I7QXrHC6cdZlDoQ7MyDNj/GKSPj5HQzIzs5m06ZNFBcXt2oDRUVFbNq0iezs7NaOTUTamXlXoeX5hl0cMcTU3Z7QPEAREYlWbb1bD62pGdD2bXkL+B4pM887jni7jfi6Ofvh4gsG6FxBJKL5HQyYMWMGFRUVXHLJJRQVFfn1nKKiImbOnElVVRXnnHNOwIMUkfZRWl1LrcesqNTcSYvZSklTBUREJLr5iuUGeLce/C/s19ZuAg2fe6SL60goHujlDV6oxpBIZIvxd8U777yTV155hZycHPr378/FF1/MKaecwqBBg0hPTyc2Nhan00lBQQFbt27l008/5fXXX6esrIy0tDR+97vfhfJ9iEgQeE9YEmJtxNmbv6uQnhDLwbIapf6JiEjUOlIbXX+l+3n3Oxjt/tL8qNlzpGl+7S1VmQEiUcHvYEBmZiZLlizh/PPPZ+fOnTz//PM8//zzR3yOYRj07duXt99+O+B6AyLSfgorj35XIbUu2q8DvIiIRCvfXfQgTBMoPEJw3FnroaymFghSsUI/pgmEu16AOQZlBohEg1b1Uhk5ciSbNm3iiSeeYPLkyTgcDgzDaLI4HA4mT57ME088waZNmzj++ONDNX4RCSJ/Uhn9rZ4sIiISqYKRUt/weGgYRrPreKvpWy2QHBeEKQnRMk1AUwpFooLfmQFesbGxzJ49m9mzZ+PxeNi1axeFhYVUVVURHx9PWloa/fr1w2oNrGeriIRPoR93FdISVSFYRESiW5EfmXBH482Uc3sMSqtqSUloerFf0OC4avX2BwyAb0rCkTIDKiMnM0DTBESiQ6uDAQ1ZrVb69+9P//79gzUeEQkjf06O0jRNQEREolwwagbE2W0kxtqocLoprHQ2GwwIRr2Ahs8vPELavXe6QkRkBmiagEhU6JC375988kn69+9PXFwcY8aM4bPPPmtx3TfffJNp06bRrVs3kpOTmTBhAh9++GGT9d544w2OO+44HA4Hxx13HG+99VYo34JIWBT4kxngRxEjERGRSFYUhJoBcPR0eF8tnjZux9v1wK+aARERDFBmgEg0CDgzIDc3l+XLl7Np0yaKioqorKwkISGBtLQ0hg4dyoQJE+jTp08wx+qXBQsWcMstt/Dkk08yadIknnnmGc4++2w2bNjQ7Hg+/fRTpk2bxv33309qaiovvPACP/jBD/jqq68YNWoUAMuXL2fmzJncd999XHDBBbz11ltcfPHFfP7554wbN66936JIyNTXDGh5XqNqBoiISDSrdrmpcLqBtl84pyfGsqeoqsWL9PoL9LZV+G9NN4G2Bh6Cwft3ran1UOV0Ex/bfIciEQmvVgcDlixZwl133cXy5cuPuu748eO57777mDJlSkCDC8TDDz/MNddcw7XXXgvAvHnz+PDDD3nqqaeYO3duk/XnzZvX6Of777+fd955h/fee88XDJg3bx7Tpk3j9ttvB+D2229n6dKlzJs3j1dffTW0b0ikHdWnGDpaXEfRfhERiWbe45fNaiE5rk0zZus7CrRwTPTnuNqa7Rzp2BuswEMwJMbasNssuNwGRZVO4mPjwz0kEWlGqz4B//znP3PHHXf4KqbGxsbSu3dv0tPTiYuLo7q6msLCQnbv3o3T6WT58uVMmzaNP/7xj74L6VByOp2sWrWK3/72t40enz59OsuWLfPrNTweD2VlZaSnp/seW758Obfeemuj9c4888wmgYSGampqqKmp8f1cWloKgMvlwuWK3PlT3rFF8hgldAorzH022WFtcR9IcpiziwrLnRG5n2gflo5A+7FEu0jehw+UVALmvPba2to2vVZqXTDhUFlVs+/1UHl13Xq2Nv0tkmLN4oOVTjdlldXE2ZveafdmDSTHtnwMb0+p8XYOljs5UFJJt8S2BV3CJZL3Y5Ej8Xef9ft/5uLFi/nd734HwKWXXsr111/P+PHjsdubRh9dLhdffvklTz31FK+99hp33nkn48aNC3mGwKFDh3C73WRmZjZ6PDMzk/z8fL9e46GHHqKiooKLL77Y91h+fn6rX3Pu3Lncc889TR5ftGgRCQkJfo0lnHJycsI9BAmDPQdsgIXN61bh3tX8OoeqAWI4VFbFwoUL23F0raN9WDoC7ccS7SJxH95cbAFs2N01bT6OFR+wAlZWrdtMz9KNTX6/fov5+7xdW1m4cEvA2zEMsFpseAwLb/73Q1IPSzTwGFBUYR7Dv/nyM7aFf6YANrc5nkWffMGu1OZbL0aLSNyPRY6ksrLSr/X8DgbMmzcPi8XCY489xg033HDEde12O5MnT2by5MlMmDCBm2++mUceeaTdpgtYLI1btxiG0eSx5rz66qvcfffdvPPOO3Tv3r1Nr3n77bczZ84c38+lpaX07t2b6dOnk5yc7M/bCAuXy0VOTg7Tpk1rNtAjHdsf1iwBXJw95RQGd+/S7Dpl1S7uW70Ep8fClGlnNnt3Ipy0D0tHoP1Yol0k78Oeb/Ng4zr6ZKUzY8aJbXqtXUu380neVlKzejFjxvAmv19w4GsoKGTS2OOZcUJ2m7b1p+8+4WC5kxPGn8xxPRqfS5ZUufB8uQSAH597FrEx4a8R/lLeSvJ3FnHMiFHMGJEV7uEEJJL3Y5Ej8WalH43fwYAVK1aQnZ191EDA4X75y1/ywAMP8NVXX7XqeYHIyMjAZrM1uWN/4MCBJnf2D7dgwQKuueYaXn/9dc4444xGv8vKymr1azocDhyOpvPD7HZ7VHyYRMs4JXhq3R6Kq8yUou4pCS3++6fFxBBjtVDrMSh3GSQ100opEmgflo5A+7FEu0jch0trPAB07eJo89gyksy58CVVtc2+VnGlOQ0hIzm+zdtKT3RwsNxJWY3R5LXKSswpAkmOGBLj21afIFi8dQ7KatwRtw+0ViTuxyJH4u/+6nfYsLS0lKyswKJ6WVlZlJWVBfTc1oiNjWXMmDFNUnlycnKYOHFii8979dVXufrqq3nllVc455xzmvx+woQJTV5z0aJFR3xNkWjjDQSAOc+vJRaLxVcluKhCc+hERCS6FPrRRtdf3u47LVX5LwpihX9vYcDmihUWRlBbQa/6ooc6VxCJVH5nBmRnZ7Np0yaKi4tJTU31ewNFRUVs2rSJ7Oy2pUb5a86cOVxxxRWMHTuWCRMm8Oyzz5Kbm8v1118PmOn7e/fu5cUXXwTMQMCVV17Jo48+yvjx430ZAPHx8aSkpABw8803c8opp/DAAw9w3nnn8c477/DRRx/x+eeft8t7EmkP3irEqQl2YmxHjhOmJdg5WFajjgIiIhJ1Cn1tdIMRDDDvwjd3wWsYRpC35Q3ENz32FkVgMCBV3YdEIp7fmQEzZsygoqKCSy65hKKiIr+eU1RUxMyZM6mqqmr2jnsozJw5k3nz5nHvvfdywgkn8Omnn7Jw4UL69u0LQF5eHrm5ub71n3nmGWpra7nhhhvo0aOHb7n55pt960ycOJHXXnuNF154gZEjRzJ//nwWLFjAuHHj2uU9ibQH3wmLH3cv/Ol3LCIiEom8d9ZDnRlQ5XJTU+upWy8ImQFHOPYW+jIQIieVPa1uLMXKDBCJWH5nBtx555288sor5OTk0L9/fy6++GJOOeUUBg0aRHp6OrGxsTidTgoKCti6dSuffvopr7/+OmVlZaSlpfk6EbSH2bNnM3v27GZ/N3/+/EY/f/LJJ3695oUXXsiFF17YxpGJRC5v5N6fuwreE5JiRftFRCTKeO+id+0SvAv0kioXtW5Po8w670V7bIyVhNi2F9utT7uPrswA3TgQiVx+BwMyMzNZsmQJ559/Pjt37uT555/n+eefP+JzDMOgb9++vP322wHXGxCR9lFYN//fnzsl3pONQtUMEBGRKBPMmgEp8XYsFrP1X1Gli25J9cX7Gmbc+dPV6mj8ywyInGCAbhyIRL5W9R0ZOXIkmzZt4oknnmDy5Mk4HA4Mw2iyOBwOJk+ezBNPPMGmTZs4/vjjQzV+EQkSX5GjxKOnGHrX0TxAERGJNvXHu7ZfOMfYrKTEN39MDHZRv2jLDPBOE1ABQZHI5XdmgFdsbKwvDd/j8bBr1y4KCwupqqoiPj6etLQ0+vXrh9Ua/v6mIuK/gvLWTxNQMEBERKKJYRi+TjhBu0hPiKW40tXkjn1rguz+OFJWnvexYAQ4gkUFBEUiX6uDAQ1ZrVb69+9P//79gzUeEQkT78G6ayuCAZoHKCIi0aS8phanu66oX5BS6tMTY9l+qKJJlf/6C3RHc09r/Xa8F9fNdRMIYlHEYPEGJsqqa5vUUxCRyKD/lSICtG4OpfcArwrBIiISTbxZAXF2K/FBKOoHDe7YH3YHvMhXMyBYmQF233YMw2h+WxGUGeCtpwBQXKXzBZFI1KbMgObs3buX1atX43a7GT16NL179w72JkQkBFozhzI1oeVWSiIiIpGq0JcFF5y79dDyHfvCVnTp8Ws7da/jrPVQ6XST6Kg/jS8M8pSEYLBZLSTH2SmpclFU4SSjS/D+5iISHK3KDKisrOTWW28lOzublJQUpk+fztq1a32/nzNnDv379+e8887jRz/6Ef369ePyyy+nsrIy6AMXkeBqTaGjIxUxEhERiVT1hfaCd9HsPW4WHB4MKA/u3fp4uw1HjHnq3jAYX+v2UFLlf0eg9qQigiKRze/MAMMwOPfcc1m6dKkvNemjjz5iypQprFy5kn//+9/MmzevyfNee+01ysrKePfdd4M2aBEJvqIGLZCOxnviU+l0U+1yE2cPTqqliIhIKAWzraCXr8NOS5kBQdqWxWIhPTGWvJJqiiqd9E5PAKCkyoVhgMWCr7NBpEhNiIWCSt08EIlQfmcGvPLKK3zyySdYrVauv/56HnvsMX784x9TVFTE/fffz7x580hKSuLll1+mrKyMwsJCnnrqKRwOB//73//44IMPQvk+RKQNql1uKpxuwL/MgCRHDDFWcyKg6gaIiEi0CGZbQS9fUd3DjoehmMffXAFf73tKibdHXJE+b2ZAsYIBIhHJ78yAl19+GYvFwpNPPsmsWbMAuPHGG/n5z3/O3//+dwCee+45Lr30Ut9zfv7zn1NbW8svf/lLXnnlFc4666wgD19EgsF7IhFjtZAcd/SPBYvFQmpCLIfKayiscJKVEhfqIYqIiLRZQQgyA7p2ab5mQCgCD81N0/N1LYiwKQJQf4NB0wREIpPf4cM1a9aQlJTEz372s0aP33jjjRiGQWJiIldddVWT5/3sZz/D4XCwYsWKto9WREKiYb0Ai7f071F40yIV7RcRkWjRXnfrPR7DdwEc1G15OxdU1F9ct6bmT3vz/m00TUAkMvkdDCgoKKBv377YbI3nBg8cOBCg2d8BxMfHM3jwYPbt29fGoYpIqBQFcFch1ZcWqQO8iIhEh8IQBAOau1tfVl2L22PW2EoNUmtBqG9TWNTMNIFIKx4IDaYJVCgzQCQS+R0MiI+Px2ptunpCglm8JC0trcXndunShZqamgCGJyLtob79kf8nLC21UhIREYlUIakZcFhRXYCCCvO8t4sjBkdM8IrsNheIry+KGFnFA0E3DkQind/BgIyMDPbu3RvQRsrKyujatWtAzxWR0AskbVLzAEVEJNqEoptAw6K63tcvCiDI7g9fFkLDzIAQZDsEi/fvrCmFIpHJ72DAoEGDKCgo4MCBA01+53K5+OSTT5p9nsfjYfv27WRlZQU8SBEJrUAKKnnvQBQqM0BERKJEKObxWyyWBnP5nXVfQ1PUL625AoKVkVwzoG5ag24ciEQkv4MBY8aMwTCMZi/6bTZbs1MIAJYvX05VVRUnnXRSwIMUkdDy3lXo2ooTiebmSIqIiEQqt8cI2R37rocdE0N1t75+il79xbVvWxFYMyBVmQEiEc3v1oKXXHIJCQkJdO/evVUbeOaZZwA49dRTWzcyEWk3gdxVSE3QNAEREYkeJVUuDLOmX9CL7R3eUSBUd+u9QYxGNQPqjsORmBngDYYUV7owDMPvjkUi0j78DgaMGDGCESNGtHoDL774Ii+++GKrnyci7SeQOxje1oIqICgiItHAe6GeFBeD3eZ3cqxfDp/LH6q79Q234724rj+GR2IBQXNMtR6DsppakuMib4winVlwPwlFJCoFUlCpub7KIiIikcqbwt+aKXH+qr9jb96l9x1Xg50ZUHfs9V5cQ33gIRJbC8bZbcTbzW4Kai8oEnkUDBCRgFotqUKwiIhEk1BdoEN9BkBhXUvBwhDVDIiz20iINS+uiyqcOGs9vqBAJHYTgAYFh3W+IBJxFAwQ6eQMw/AVImrNCZJ33Qqnm5pad0jGJiIiEiyhLLTnq/Jfdzz11QwIxbYaZOZ5A/JWCxGbgl9fY0jBAJFIo2CASCdXXlOL0+0BWneClBwXg62ur3KxigiKiEiEKwhlZsBhrQV9XXq6hG5bRZXORkEHqzUyi/N5p1Aok1Ak8igYINLJee9ixNttxNelHvrDYrHUp/6pboCIiES4ULX7a/ia3rvfgdTi8VeaL/DgCunUh2BJbaYdoohEBgUDRDq5wgDqBXilJdRXNRYREYlk7ZW673J7KK0O3Tz+9IT6bj7ezLxQTH0IlnTVGBKJWH63FhSRjslXhTiAlkS+OZKaJiAiIhHOl7of4swAb3aAxQIp8cGfx+/LDKh0+jL6AjmGtxdvFqHOFUQij4IBIp1cW1IZVSFYRESihbftXyhS6r3HUJfbYHdhJQCp8XZfbZ1gSm+QlZdQ17YvUjsJQP00AZ0riEQeTRMQ6eQCaSvo5bsTomkCIiIS4eprBgT/Lnp8rI34ugvzrQfKgdDN409rUKwwlFMfgkUFBEUiV8DBgPPPP58nnniCDRs2BHM8ItLOCtpQUClN7YJERCRKhLKoH9QfR7cdrABCMx2h4XaKKp0hLYoYLCogKBK5Ag4GvPvuu9x8882MGDGC7OxsLr/8cv7xj3+wa9euYI4vIE8++ST9+/cnLi6OMWPG8Nlnn7W4bl5eHpdddhlDhgzBarVyyy23NFln/vz5WCyWJkt1dXUI34VI+2hL32UVEBQRkWhQU+umvCZ0Rf0avu42b2ZAiIIODYsV+qY+RHJmgAoIikSsgIMB8+bN44c//CEpKSnk5+fz6quvMmvWLAYMGMCgQYO47rrrWLBgAQcPHgzmeI9qwYIF3HLLLdxxxx2sXr2ayZMnc/bZZ5Obm9vs+jU1NXTr1o077riD448/vsXXTU5OJi8vr9ESFxcXqrch0m7a0paovoiRov0iIhK5vFX3bVYLyXGhKbaX5ssMMIMBoQ46FFW6oiIzwFfjQOcKIhEn4GDATTfdxFtvvUVBQQErVqxg7ty5TJ06lbi4OLZv387f//53LrvsMrKysjj++OOZM2dOMMfdoocffphrrrmGa6+9lmOPPZZ58+bRu3dvnnrqqWbX79evH48++ihXXnklKSkpLb6uxWIhKyur0SLSEbStZoDmAYqISOSrnyJgxxqCon5Q3/Ivt66AYOhqBtQfew+V14R0W8GQWjfeKpebapc7zKMRkYbaXEDQYrEwduxYfvOb37Bo0SKKiopYsmQJd955JxMmTABg3bp1PProo20e7NE4nU5WrVrF9OnTGz0+ffp0li1b1qbXLi8vp2/fvvTq1Ytzzz2X1atXt+n1RCJFW+ZQpjZIVRQREYlURSGuFwD1F+Qew/w5kOl3fm0noX47+aXVId1WMCQ5YoipC8AUKztAJKIEvbVgQUEBu3fvZvfu3eTm5mIYRrA30aJDhw7hdrvJzMxs9HhmZib5+fkBv+7QoUOZP38+I0aMoLS0lEcffZRJkyaxdu1aBg8e3OxzampqqKmp8f1cWloKgMvlwuWK3A9C79gieYwSXN4L+WSHpdX/7smxZjyxqMIZMfuM9mHpCLQfS7SLtH34YGkVAKkJ9pCNKSWu8Wl1cpw1ZNtKiouhrLoW72l2l9jWH8PbU0q8nYIKJwdKKumaYAv3cPwWafuxiL/83WfbHAwoKSlhyZIlLF68mMWLF7N582YADMMgMTGRs846i6lTpzJ16tS2bspvFkvj9C/DMJo81hrjx49n/Pjxvp8nTZrE6NGjefzxx3nssceafc7cuXO55557mjy+aNEiEhISAh5Le8nJyQn3EKQdeAworrQBFr5Z/hlbW3ljobIWIIYKp5t3/7uQmAhqVqp9WDoC7ccS7SJlH/4s3wLYqCktYOHChSHZxr795ja8tm34loV5a0OyLYdhowzz3NZmMfh08SLacKobcjEe81zjw08+Z0dK+90oDJZI2Y9F/FVZWenXegEHA26//XYWL17M6tWr8Xg8GIaB3W7n5JNPZsqUKUydOpXx48cTExP05IMWZWRkYLPZmmQBHDhwoEm2QFtYrVZOPPFEtmzZ0uI6t99+e6M6CaWlpfTu3Zvp06eTnJwctLEEm8vlIicnh2nTpmG3h6bAjkSOwgonxpefAPDjH5yF3da6q3nDMLhz1Ue4PQbjTplCZnL4i2pqH5aOQPuxRLtI24e3fbwNdmzj2AF9mDHjuJBsw7p+P//eXn/xP/3UiRzfq+V6VG3xj91fcWhPCQBdu8RxzjmnhmQ7wfKvfSvYv6uYISNGcfbw6Km7FWn7sYi/vFnpRxPwlfoDDzyAxWKhV69eXHLJJUyZMoXJkyeH9a53bGwsY8aMIScnhwsuuMD3eE5ODuedd17QtmMYBmvWrGHEiBEtruNwOHA4HE0et9vtUfFhEi3jlLYpc5pTWZLjYkiIa7q/+iMtwc6hcidlToNeEbTPaB+WjkD7sUS7SNmHS6rNtoIZSY6Qjadbcnyjn7snJ4RsW1271B+z0xNjI+JvfCRpieZ4S2s8ET/W5kTKfiziL3/31zbdtjcMgz179vDOO+9QXl5OZWUlp512GmlpaW152TaZM2cOV1xxBWPHjmXChAk8++yz5Obmcv311wPmHfu9e/fy4osv+p6zZs0awCwSePDgQdasWUNsbCzHHWdGju+55x7Gjx/P4MGDKS0t5bHHHmPNmjX87W9/a/f3JxJMbekk4JWaEMuhcqfvtURERCKNtwVuemJggW9/HH4s9Vb9D4WGhRBDWRQxWLwFDtV9SCSyBBwM+OKLL/j4449ZvHgxy5cv56mnnuLpp5/GYrFw/PHH++oEtHe2wMyZMykoKODee+8lLy+P4cOHs3DhQvr27QtAXl4eubm5jZ4zatQo3/erVq3ilVdeoW/fvuzcuROA4uJirrvuOvLz80lJSWHUqFF8+umnnHTSSe32vkRCwddJoA3BAF//4AoV1xERkcjk7SaQ3k4X6HabhS6O0E2Vbfg+2hLQby/e9oJF6iYgElEC/pSaMGECEyZM4I477qC6upovvvjCV0Twm2++YfXq1Tz00EPY7XbGjRvH1KlT+f3vfx/Msbdo9uzZzJ49u9nfzZ8/v8ljR+t48Mgjj/DII48EY2giEcV3ctSGuwppvgO8ov0iIhKZ2tJG11+pCfUX6GkJsW0qXn00DYP4ocxACJY0340DnSuIRJKg1P6Oi4tj6tSp3H///Xz11VcUFBTw9ttvc+GFF+Jyufjss8+arawvIuFVGIRpAjrAi4hIpCusaPvx7mjsNisp8faQbwcaB/HbEtBvL2kJunEgEomCmr9UVlbGJ5984ssQ2LBhQzBfXkSCrLA8CMGAuucW6gAvIiIRyDAM3zEq1PPr0xNjKalyhTwY0DgzIPKDAaneGweaJiASUdoUDHA6nY2mB6xatQq32+1Lu4+JieHEE0/0tRoUkcjiOzkKQs2AYh3gRUQkAlU63ThrPUDo79inJdjZQegv0Bu+j2ioGZCmAoIiESngYMC0adNYtmwZ1dXVgBl1tVgsjBw50nfxf8opp9ClS5egDVZETNUuNx9vOkCV092m19mUVwa0LcXQO0eyUNMEREQkAnmPT44YKwmxtpBuy3thHurU/WjrJlA/TUA3DkQiScDBgMWLFwMwePBg38X/6aefTteuXYM2OBFp3iMffc8zS7cH7fUyktqQGZDoTf1TMEBERCJPQYPigaEs6geQ0cXR6GuoRF1mQN0YS6tduD0GNmto/x1ExD8BBwPmz5/P1KlT6dmzZzDHIyJH4az18PrXewA4sV8aCbFtK/3RIyWOiQMzAn5+moIBIiISwfYWVQHQMy0+5Nu6elI/3B6DH48J7flxSrydGKuFWo9B1y6RHwxIrSusaBi0S00FEfFPwFcRV155ZTDHISJ++mjjfgornHRPcvDqrPHE2ILSFCRg9d0ElPonIiKRJ7ewEoDe7RAMGJqVzF8uOj7k27FZLfxuxrEcKq+hR0ro31dbxdisJMXFUFZdS2GFs03BgF0FFRwqdzKmb1oQRyjSOQWtm4DL5WL79u2UlpaSnJzMgAEDsNsjv++pSLR5beVuAC4a2yvsgQDAd0eivKaWl77cxU/G9w3ziEREROp5gwF90hPCPJLg+tnJ/cM9hFZJS4ilrLq2TUUEF6zM5a531uOs9fDiz07ilGO6BXGEIp1Pm68kVqxYwbnnnktycjLHHXcc48eP57jjjiM5OZkf/vCHrFy5MhjjFBFgb3EVn205CMDFY3uHeTSm5Dg7V0/sB8Cdb3/Hwznf+zqKiIiIhNueorrMgA4WDIg2bSkiWFPr5vY3v+U3b6zzdYb40/824vbofEOkLdoUDHjuuec4+eSTef/996mpqcEwDN9SU1PDf//7XyZNmsTzzz8frPGKdGqvf70bw4AJA7rSt2tiuIfj84cfHMdNUwcD8NjiLdz+5jpq3Z4wj0pERKTjZgZEm9S6aYUF5TWtet7e4ioufno5r67YjcUCv5wyiNQEO5v3l/Hvr3eHYqginUbAwYA1a9Ywe/ZsamtrmTBhAm+//Tbbt2+nqqqK7du388477zBx4kRqa2v5xS9+wZo1a4I4bJHOx+0xfIUDLzkpMrICvCwWC3OmHcMfzx+O1WJOZbj+pVVtbn0oIiLSFrVuj6+AYJ+uCgaEk7fDwu/fXc/tb37L1gPlR33OF1sP8YPHP2ftnhJSE+zM/+lJ/Gr6EG6aYt6AeGjR95TX1IZ03CIdWcA1Ax566CHcbjc33XQT8+bNa/S7fv360a9fP37wgx8wZ84c5s2bxyOPPMI///nPto5XwqmqGL77D6z7D7iqIGMwZBwDXQfVfR0I9sgvYhOtvth6iL3FVSTHxXDmsKxwD6dZPxnfl4wuDm56bTUfbTzA5X//kuevOtHXcUBERKQ95ZVUU+sxiLVZyUyKC/dwOrVZp/Rn64Ey1u4p4dUVu3l1xW6mDO3OtZP7M2FA10ZtHw3D4Oml2/nLh5vwGDAsO5mnfzLGN9XjJ+P78uLynewsqOSZpdv41fQhrR7Pt3uKWb+v9IjruN1u1u23UPb1Hmw2W6u3EWxZyXGcNqRbyFtkSucRcDDg008/JTk5mQcffPCI682dO5d//OMffPLJJ4FuSsLJ44Gdn8Lql2Dje1BbXf+7vDWHrWyBlN51QYLBYE8AtxPcrrqvDZe6xwxP3WKYX6HxY1Yb9BwNA6dCnwlg77wH8gV1hQMvGNWTOHv4D0gtOWt4Fi9fO45r5q/km9xiLnpmOf/82Un0TFWgSERE2tfuuikCvdLisaq3fVgNzUrm7RsmsXJnEc99tp2PNu7n400H+HjTAYZlJzNr8gDOGdmDapeb/3t9LR+u3w/ARWN6cd/5wxud+8TGWPnt2cdy/UurePbT7Vx6Uh+yW3Ge8b9v87jhlW/8XNvGgu0bWvNWQ+r16ydwYr/0cA9DOoiAgwH79+/n+OOPJzb2yHf8HA4HQ4YMYe3atYFuSsKhZDd8929Y/TKU5NY/3u1YGH0FpPaBQ1ugYCsc+t78vrrYXLckF7YtDt5Ydn0Byx6HmHjoN8kMDAycAt2GQCeJjBZWOFm0IR+Ai0+MrCkCzTmxXzr/+cVErvrHCrYeKOdHT37BP392EkOzksM9NBER6UR2q3hgRLFYLJzUP52T+qez41AFz3++nf+s2sP6faXcsmAND3ywCUeMlZ0FlcTarNz9w2FcelLvZu+Enzksk5P6p7NiRyF//XAzD888wa8xrN9Xwv+9bl6XjO6TSte66QvNMTwe9u/fT2ZmJhZreDs4rc4t5lB5DTsPVSgYIEETcDAgMTGRgwcP+rXuwYMHSUjQh3DEc1VjWf82E7Y+RszqDUBdhVZHMoy4EEb9BLJHN38BbhhQcQgKttQHCTy1YLODLbZuOex7q92882+xAhbzdS2WBj9bwVkOOz+HrYuhPB+2fmQuAMk9YeDpZmCg78mQlNlOf6j29+Y3e3C5DUb0TGFYdkq4h+OXYzKTeKMuILDlQDkXPb2cG08fhCMmtAdTt8fDhjwLh77MxXaEA3d6FwczhmdFRHtGEREJDRUPjFz9MxL54/kj+NW0Ibz81S7mL9tFXomZgdojJY6nfjKGE3qntvh8i8XCneccyw+f+II3V+/l6kn9GNmr5fUBDpXXcN2Lq6hyuZk8OIMXrj7xiOcBLpeLhQsXMmPGqLC3TL/5tdW8s2YfxQF0YxBpScDBgBEjRvDZZ5/xySefcNppp7W43scff8zOnTs59dRTA92UtJfKAmxvX093bxCg/ykw6goYei7EHuUgarFAl27m0ndicMd1wmVmsOHABtj2sRkY2LUMSvea0xdWv2Sul9bf3Haf8eaUgq6DOkTmgGEYvmq5M6MgK6Ch7NR4Xr9+ArNe/JqVO4uY+/6mdtqyjTd2Hn1bMZePZsaIHu0wHhERCYfcQrN4YO90TVULKY/HzBCNSzFv9LRCWmIsN04ZzKxTBvDOmn1s2V/Gz08d6Cs4eCQje6VywaievLV6L3/830YWXDe+xfn0zloPs1/6hr3FVfTPSOSJS0dH1Q2BtLpuDEWVzjCPRDqSgIMBV1xxBZ9++ikXXXQRjz32GJdcckmTwh+vvvoqN998MxaLhSuuuCIoA5YQSumJcfxlbN5fwcAf34W926Bwj6iexQKZw8xl4i/NAoa7voBtS2D7J7B/PRTtMJc1L5vPScioDwwMOBWyRoT1LQRq9e5ivt9fTpzdyg9PyA73cFotNSGWf10zjieXbGX7oYqQb8/wGOzL20d2j2wsLcwPXbunmN2FVb65pCIi0jEpMyBIPB6oOADFuXXLrgbf50LxbnDXmNmfKb0hrS+k9YPUuq/en+PTWtyEI8bGxWNbf9Pj12cOYeG6PFbsKGTRhv3NFlk2DIM/vLueFTsLSXLE8NyVY0lJCO+d/tZKrRtvUQRkBng8Bu+s3cvxvVIZ0K1LuIcjbRBwMOCnP/0pr776Kh9//DE/+clPuO222zjuuOPIysoiPz+fDRs2sG/fPgzDYOrUqVx99dVBHLaEivvcR9m8cCEDU/uGeyhHZo+HQWeYC5idDvashNzlsGs57F0FlYdg03/NBcwsh7PmgiMpbMMOxIIVZlbAjBE9SI6LrgOXV5zdxpwAKv0Gwkzp28OMGSNbTOm7970N/OOLHRQqui4i0qF5g76qGRCAwu1mNubWj8wpm86jtwLE7YTCbebSHEcydOkOXTIhsZv5fWJ3M7M0se7x1N7m437KTo3n2sn9+duSbfz5/U2cPqQ7sYdNSXzpy128uiIXiwUeu3QUg7pH3wWsNzOgOALOXb7aUcitC9ZyQu9U3r5hUriHI20QcDDAarXy7rvvcsstt/DCCy+wd+9e9u7d22gdm83GNddcw8MPP4w1zEU3pIOLT4XB08wFoLYG9q2B3GXmlIItObD6X7BjKVzwTPCnMoRIeU0t7327D4CZAUTLpXlpddH14orwR9dFRCQ0ymtqKawwL5wUDPBDTTns/Kw+AFC0o/HvLVazXlNqn+aXLplQfsDMGijaBUU7G39fcQBqSs2lYOuRxzJoGoy/3iwa7ceUz1+cNogFK3ez41AFL3+1i59O6u/73fJtBdzzntkN4DdnDeX0of4HGiJJfWZA+IMBe4vN6Tff7immvKaWLo6ALyklzNr0L5eQkMCzzz7LXXfdxQcffMCmTZsoLS0lOTmZoUOHcvbZZ9OrV69gjVXEfzEO6DPOXE6+1Yxov/ULM5XthRnmVIPT74j4VoX/+3YflU43/TMSOam/KscGS1qi5t2JiHR03qyAtAR71GbWhZRhwMFNsGWRefG/azl4GgTJrTHmVMuBU2DQVOh+nFkA+kjS+ppL/2Z+56yEkj1Qvt8MDJQfrPt6ACoOml/LD5g1obbmmEvGMTDu5zDyEnC0fDe/iyOGOdOG8Lu31vHo4i38aFQvUhLs7C6sZPbLq6j1GJx/QjY/P2VAYH+rCOCrGRABNzKK6oJsHgPW5BZz8uCMMI9IAhWUME7v3r2ZNWtWMF5KJDT6nQy/+AI+/J2ZIbDsMfPAd8Ez0GNkuEfXogUrzSkCF49tvq2OBKY+1S78B1QREQmNXE0RaMpVBTs+NQMA3y9q3D4azDn+g84wL/77nxLcqZWxCdDtGHM5koJtsOJZs731oe/hf7+Cj+41W1ufdJ0ZbGjGxWN7MX/ZDr7fX87jH2/h1mnHMOvFrymqdDGyVwp//vHIqD6XSo+gGxkNp1l+vatQwYAoppwO6TzikuG8J2DIDHjvJrM7wXNT4PTbYdItra5+G2rf7y/jm9xibFYLPx7TM9zD6VDSIijVTkREQkP1AuoU58L3H5oBgB2fQm11/e9sDug/2UzLHzwN0geEvxNT14Fw9gNmBueaV2DFM2b9guVPwJdPmudxJ1wO6f0hKQviUsFiIcZm5XczjuXqF1byz+U72Zhfyqb8MrolOXj2irHE2SPrPK+1vNMEiitdGIYR1sCGNzMAYNWuorCNQ9pOwQDpfIbOgN4nwXs3m8UFF99rHiTPf8o8AEUIb1bA1KHd6Z4U2dMZok2qrz2PMgNERDqq3R29k0CtE8r21aXX769bDjRY9kNZPpTuafy85F5wzHQYfKYZCIhNDM/4jyYu2awbcNJ1ZiDjq6dh+5LGxaEBYuLNoEByNqclZfFkNwurCh1s296TFNtxPHPFRLJSov88ypvV6HR7qHS6SQzjPP3CBsGA1bnFuD0GthY6OElk82svstnaHkmzWCzU1ta2+XVEgiIxA2a+BGtfhfd/A7u/gifHw3HnwdifmXPkwhhxral189ZqsyDnzBNVODDY0hK90XVn2KPrIiISGh2mrWBNuZkuf+h7c47/we/h0GYo3AGG++jPt1ih9zgYPB2OOdOc+x9Nxz2rFYacZS4HNplTCHYtg7I8qC6G2qr69tLADGBGXWkDt9WO7ZNJZiFCb92DaHrvDSTE2oi1WXG6PRRVOsMaDGiYWVleU8um/FKGZaeEbTwSOL/2IsMw2ryhYLyGSFBZLHDCZWY9gXd/Cds/gXWvm0u3Y82gwPEzIa79P9w+2nCAwgonmckOTj2mW7tvv6PzRtdrPQZlNbUqLCUi0gFFTDDAMMyieLtXmG2Q96w0095tsWbB45i45r86K8wL/8Pv7DcUE1ffqq9L5mFt++oeyxgE8Wnt935DqftQOPfh+p9dVWZQoCwfSveZX8vy2L93O0mH1pJQudc8v9v+CeTcBUk96gMDA06DhOgpzmyxWEhNsHOgrIbiShe9wvhP6s0MSIy1UeF0882uIgUDopRfwYAdO3Y0+/jbb7/NbbfdxoABA7jpppsYNmwYmZmZ7N+/n/Xr1/P444+zfft2HnzwQc4777ygDlwkaFL7wJXvwN5vYNULsO4/cHAjvP9r+OgPMOJCMzCQPardhrTga3OKwIVjehFjU1vOYIuz24izW6l2eSiucCkYICLSwXg8BruLzPZnvdOCEAzwuMHtBLfLvJlgsYLFZtYbsljrlro7zq5qyFtjXvR7AwBleW3bfmI3yBgC3eqWjGOg21AzPT5K73QHhT3erHOQ3rhLQCaYQZiCrfWtEnd+bv47rHnJXCxWs1bCmX+CjMFhGX5rpSXEcqCsplGafjh4p1meOqQbC9fl8/WuIq6Y0C+sY5LA+BUM6Nu3adXO5cuXc9ttt3HllVfy7LPPNkqzHTJkCKeccgq/+MUvuO666/j1r3/NuHHjmn0dkYjRc7S5TLsPvv03fP28mY73zYvmkj0aTrzWDA7EOALaRK3bw3Of7eBAWXWL63g8Bp9tOQiYXQQkNNITYtlXUk1RpZM+XaM8hVRERBo5UFaDs9aDzWqhR6of88ULtsF/b4GineYFv/fC3+00F8Pjx1YtZnDA8DRd32KDrOHQ6yTodSJkDjPXqa0xC/r5vjb43maHroPNi/8ouoMdMSwW8yI/Y7BZe8BVDbnL6oIDi80bP1s+hG0fw4TZcMqvg9s9IQS80xzDWQDZ7TEortv+GcdmmsGAnSoiGK0Cnmxy//33ExcXx+OPP37E+baPPvoor732Gvfffz/vvPNOoJsTaT/xqTDuOjhpFuQuh6//ARvegX3fwDuz4aO7zWI2J17T6oPzqytyeeCDTX6tO3FgV/p2jdCiPh1AaoNggIiIdCy7i8wpAtmpcdiPlmFXXQKvXmLOyW8TAzx19bESu5vFinuNNQMA2aPM1noSPvY4GDjFXM78ExzaAh/eYQYEvnjUvBE07T7zpk+EZltEQmvk0ioXnrrZ31OGdsdqgb3FVeSXVHeIQo2dTcDBgK+++oqhQ4cSF3fkf/T4+HiGDh3K8uXLA91Uqz355JP85S9/IS8vj2HDhjFv3jwmT57c7Lp5eXn86le/YtWqVWzZsoWbbrqJefPmNVnvjTfe4K677mLbtm0MHDiQP/3pT1xwwQUhficSVhYL9J1oLmf9GVb/C1Y8Z877W/JH+Owhs+bAhBv86kJQU+vmb0u2AXDOiB70PcLd6BiblYvG9AraW5Gm6osIqqOAiEhHk1vgZ70AjxvemGUGApJ7wo+eA0cXcz5/o8VufrXWnTobbvO53iwAj9t8zPCYWQBdukfsBaXUyRgMl/8bNn8AH/zWLED45rXmlNGzHzQzOSJMfTek8N3IKKzbdpIjhtSEWI7tkcz6faV8vauQc0dmh21cEpiAgwHl5eUcPHjQr3UPHTpEeXl5oJtqlQULFnDLLbfw5JNPMmnSJJ555hnOPvtsNmzYQJ8+fZqsX1NTQ7du3bjjjjt45JFHmn3N5cuXM3PmTO677z4uuOAC3nrrLS6++GI+//xzxo0bF+q3JJEgMQNOvhUm3Ajr34Jlj0P+t+ZUgq//Yfa8nXjjEbsQ/HvlbvJLq8lKjuOhi4+P+n630S4SDqgiIhIafhcP/PiP5p3hmDizy1DP0e0wOokoQ84yiwkufxw+fQh2fQHPTIYTZ8HJv65fzzCgqsi8KVSy1/xauheqS+GES6HnmJAPNS0h/DcyiurqFaQlmudRY/ummcGAnUUKBkShgCuTDRo0iF27drFw4cIjrrdw4UJ27tzJoEGDAt1Uqzz88MNcc801XHvttRx77LHMmzeP3r1789RTTzW7fr9+/Xj00Ue58sorSUlpvgrmvHnzmDZtGrfffjtDhw7l9ttvZ+rUqc1mEEgHZ7PDyIvh55/CVf+FY84CDNj8P3jhbHjudPh+UZOn1dS6efITMytg9ukDFQiIAN4DalGYi/CIiEjw7a4LBvQ6UvHA796Az+sq0//wCQUCOjN7nFkz4MaVcNz5ZobHimeIeXocE7f8mZinxsH92fBgf3j6ZHh1JvxvjpkluvI5mH8u7Ap9FnRaBNzIKDwsGDCmnzlldtUu1Q2IRgEHA376059iGAYzZ87koYceoqKiotHvKysrefjhh7n00kuxWCz89Kc/bfNgj8bpdLJq1SqmT5/e6PHp06ezbNmygF93+fLlTV7zzDPPbNNrSpSzWKD/ZLhsAdywEsZcbd5V2LcaXrkIFt9npgzW+ffXe8grqSYz2aGigBGi/oCqaQIiIh2Nt2ZAi5kBeWvh7RvM7yfdDCMvaqeRSURL7Q0X/9PsMpUxBEtlAd3KN2Ap3AYuc58ioStkjTSzQk+cBX0mmr97+SKzM1Uoh+e9kRHOzIC6QER63VjG9jV7HG7IK6WipjZs45LABDxN4KabbmLRokV8+OGH3Hbbbdx555307dvX11owNzeXmpoaDMNg+vTp3HTTTcEcd7MOHTqE2+0mMzOz0eOZmZnk5+cH/Lr5+fmtfs2amhpqamp8P5eWlgLgcrlwuSL34sM7tkgeY8RJ7Q9n/RUm/xbr5w9h+/o5+OyvePatwX3+M9TEJPG3j7cA8PPJ/bHhweXypyqxBMLffTjJYWZnFJbXaH+XiKPPYol24d6HvTUDspNjm46h4iAxr16KpbYKz8AzcJ/yO9D/NWmo9yS49hM8G97lu7XfMGz8NGzpfSA527z505CrEttrM7HmLsf41wXU/uQds1tECCQ5zPu44Tx3OVhqdsRKjY/B5XLRLTGGHilx5JVUs2rnISYM6BqWcUlj/u4fAQcDbDYb7777Ln/84x959NFHKS0t5fvvv+f77+srsSYnJ3PTTTdx1113YbO1X1r04d0NDMM4YseDULzm3Llzueeee5o8vmjRIhISIr+abE5OTriHEKUm06uvjRNyn8e27SMqn5jIU0m3kl/ahxS7QfKh71i48LtwD7JTONo+nHvQAtjYsnsfCxfuaZ9BibSSPosl2oVjH3a6YX+ZeYq7edUX7Pm2/ncWTy0Ttz5ARsVeyh1ZLE34MbUffNjuY5RoEQ/pk9jzfSWwqW5pKib1aiYcOkB65Tbc83/AF4N/R3lc8OfP7ygDiGFfQclRp2qHyjc7rYCVov17WbhwNwBZMVbysPLaRyso6mWEZVzSWGVlpV/rBRwMALDb7dxzzz385je/4fPPP2fTpk2UlZWRlJTE0KFDOfnkk9v1wjcjIwObzdbkjv2BAwea3NlvjaysrFa/5u23386cOXN8P5eWltK7d2+mT59OcnJywGMJNZfLRU5ODtOmTcNut4d7OFFqBp68i7D+5yq6lO5hds3dbLX+gpOmX8F545sWsZTg8ncfTvz+IC9tXU1MQgozZkxoxxGKHJ0+iyXahXMf3nqgHFYso4sjhgt/OK3RzRvrwl9hq9iM4UjCcfVbTM8Y3K5jk+jSqv24+gyMl84nbv86pux5lNor/wupfYM6nh2HKpj33RfUEMOMGWcG9bX9tfTN7yBvH2OGHcOMUwcAcCg9l9X/20SZozszZoS+kKIcnTcr/WjaFAzwSkhIYPr06U3m1be32NhYxowZQ05OTqO2fzk5OZx33nkBv+6ECRPIycnh1ltv9T22aNEiJk6c2OJzHA4HDoejyeN2uz0qTuyiZZwRq89Y+PlS8p+/hKzClTwT+wi1NTZibHeANeBSHdIKR9uHM5LNQGVJVa32dYlY+iyWaBeOfTivzJzT3Ds9gdjY2PpfrHweVv8TsGD58T+w9ziuXccl0cuv/dieAVe+DfPPwXJwE/aXL4CffgApPYM2jm515y4VNW4Mi43YmPY/pyypMusCZCTH+/4m4wZkALBmdwk2WwxWq9pqhpu/n7sd7qpkzpw5/P3vf+cf//gHGzdu5NZbbyU3N5frr78eMO/YX3nllY2es2bNGtasWeNrl7hmzRo2bNjg+/3NN9/MokWLeOCBB9i0aRMPPPAAH330Ebfcckt7vjWJMk5HOheV38bztWcDEPP5X+G1S6G6JMwjE6jvJlCobgIiIh3K7sIqAPqkx9c/uPMLeP828/sz/gDHhPcGlnRQiRlm8cH0AVCcCy/+EMoPBO3lk+Ptvg7WxVXhOX8prCsg6C3EDDA0K4mEWBtlNbV8f6AsLOOSwAQlM6CsrIxt27ZRXl6Ox9NyYbRTTjklGJs7opkzZ1JQUMC9995LXl4ew4cPZ+HChfTta6bp5OXlkZub2+g5o0aN8n2/atUqXnnlFfr27cvOnTsBmDhxIq+99hp33nknd911FwMHDmTBggWMGzcu5O9Hotfrq3azu9TFM0nXcsXZPyR24a3w/Qfw3BQ4/Q5I6wspvSEhQ9kCYZBadxCrcrmpdrnV7lFEpIPILWzQScAwYMM78N9bwVMLwy+ESbeEd4DSsSVlwZXvmi2nC7bCi+fD1f+FhPQ2v7TNaiEl3k5xpYviShfdk+KO/qQg87ZkTk+sDwbE2KyM6pPKF1sL+HpnEUOzIndKtDTWpmDA6tWr+b//+z+WLl2KYRy5WITFYqG2tn3aTcyePZvZs2c3+7v58+c3eexoYwe48MILufDCC9s6NOkknLUenlyyDYBfnDaQ2NFnQNax8NpPzAPDfxq02rQ5zOq0Kb3M4EBKL7O1Tf9TzYCBhERyXAw2qwW3x6C40kVWioIBIiIdgTcYMNxxAP71O9i+xPxF9ij44ePQxqLSIkeV2tvMEHhhBhxYD/+6AE79DSR2g8Su5tfYLgHti2kJsRRXunwX5e2t0BcMaJyGPqZvOl9sLWDVriJ+Ml7nr9Ei4GDA2rVrOeWUU6isrMQwDBwOB927d8eqO5wi/GfVHvYWV9E9ycGlJ9UVDcweBdd9Akv+BAc2QMkeKMsDdw0U7TCXw/WZACMvhuPOD0pEWepZLBbSEuwcKndSVOkkK6X9o+siIhJ8BwoK+XXMa/xg2fvgcZlB95NvgZNvBXv8UZ8vEhRdB5oBgfkzIG+NOVW0IZujLjiQYS5JPWDQGTB4GsQmtviyaQl2dgBFle0fDHC5PZRW19aNI7bR78b2TQPg612F7T4uCVzAwYA//OEPVFRUMHHiRB5//PFGqfYinZmz1sPflmwF4PpTBzZOP+/SDX4wr/5nt8sMCJTsqVt2m18PboZdyyB3ubksvA2OORNGzoTB08GuC9dgSE2I9QUDREQkyhkGxsZ3ebr4VnrEFIAHGDQNzn7AvDATaW/dh8JV/4Wlf4bi3VB5CCoOgavSvBlUusdcvFb/C2LiYdBUOO4889wvLqXRS3ovwosq/esjH0zFddu0WCAlvnFmwKg+qVgsZs2OA6XVdE/WuWo0CDgY8PnnnxMXF8fbb79NRkZGMMckEtXe+MbMCuiW5OCycUdpJWizQ2ofczlc6T5Y9x/49t+wfx1s+q+5xKWYmQIjZ0LfiUp3bANvEcHiMBxQRUQkiA5thfdvw7JtMT0ssMfIoPtF84gddq6OkxJemcfBxS82fsxZYQYFvMGBikNwcCNsfA+Kdtaf89liYcBpZmBgyAxISPfVPArHjQzvNlPi7cTYGmeDJ8XZGZKZxKb8Mr7eVcSMET3afXzSegEHAyorKznmmGMUCBBpwFnr4YmPW8gKaK3kbJh0k7nsXw/fLoBvX4eyffDNP82l78kw40HIHBakd9C5hPOAKiIiQVC0E1b+Hb56BtxOPNZYnnDO4M2EmXwyfEa4RyfSvNhEczm8NtS0+yB/HWx81yx8eeh72LLIXCw2GHAqJ1pm8CZZYbmR4SseeNgUAa+x/dLMYMBOBQOiRcDBgH79+uF06gRapKE3G2QFXH60rIDWyBwG0+6FqX+AXV+YgYF1b8Cuz+HpyXDSLDjtdohPDd42OwFvZkC4ivCIiEgAap2weSGsmg/bPwHqCkEPnMrH/X/Fw/8t5qSuaWEcoEiALBboMdJcptwJBzbVBQbeNbNEt33MJXzMmNierN5zObh+1a5TR703T9ISWwgG9E3npS9zWaW6AVEj4GDAzJkzue+++9i6dSuDBg0K5pgkjC79+woKC238a98KLEqra7Xv95cDQcgKaInVBv1PMZdTfwMf3mEeJL562pxScMbdcMLlalXop3DOuxMRkVY6tNXMilvziple7TXgdBj3czjmLDYt2QoU0zstIWzDFAma7kPN5dTboGAbrHoB11f/YDB7GbzvQZg3H8ZdB2OvaZdC04UV5vnS4cUDvcbUFRFcv6+UKqeb+Fh1aop0AQcDfvOb3/Duu+9y8cUX85///IcBAwYEc1wSJl/vKgYsbC8rDvNIoldmsoPLTgpiVkBLUvvAzH/Bto/h/d+YqWTv3girXoAZf4Weo0M/hiinaQIiIhHOVW3Oo/7mn7Dzs/rHu2TBqMth1BWQ3t/38O7CKgD6pCsYIB1M14Ew/Y98knElX70xj+sdH5JRcQA+/iN89jCM+gmM/wWkh+6azHu+dHhbQa9eafFkJjvYX1rD2j3FjB/QNWRjkeAIOBjw4IMPcvrpp/P4448zbNgwzjzzTIYMGUJiYsutMH7/+98HujlpJ09ccjzffPMNo0ePJiZG0bxAjOyV2r6R0IFT4PovYMUz8MmfYe8qeG4KjL7SnFaQ2MYPYlcVeNzg6BKc8UYQ78FMBQRFRCLQ/vXw2uX1rXctVrP12pirYfCZYGt6GptbWAlAn65qISgdU5eUrvzdfQ5LE35EzvRCWPaYWWdgxbNm/Yze481zNqvd/D9itZuFCH3f28GRBIndoUt36JJZ97U7xHY5YsHNwoojTxOwWCyM7ZvO/9blsWpXkYIBUSDgYMDdd9+NxWLBMMx5Wu+++26LaeWGYWCxWBQMiAJnDsvEvcvgzGGZ2O3NR/0kAsXEwsRfwoiLIOf3Zk2Bb/4J370J2SdAtyHQbShkHGN+7dK9+Q/7sv3mnLT8dZD/nfm1YAtggV4n1vW/PQOyju8QUxGUGSAiEqHWvw1vzwZXhZkFMPan5p3PlF5HfJovGKDMAOmg0upuZBRUGTDyYvPcb8dSWPY4bP0IcpcF/uIx8fUBgvhUsCeYAYLYBIhNZMyuUgybk4nF/WDdevM5hgc8teaNI8PNZbYDpNv2kvrt5xDfB7JGmOeQMY42v/dmuaqgqhgMNyT1MKfUit8CDgZceeWVmlMuEmmSsuBHz5p3TRbeZl7Y7/yscWolQFxqXYBgCDiS4cAG8+K/4kDLr737S3NZ8kdIyDB74A6aZmYmtDX7IEy8c96UGSAiEiE8bjPt+fOHzZ8HnAYXvuDXfGhnrYe8EnOaQG8FA6SDqj93ceLxGFitFvP/yYDTzIKDeWvA7QKPC9y1dV9d9V/dLqgphfID5lJR99VZDrVVULzLXJoxA5hhBzbXLc2YBEyyA0XAB3UP2hOgz4T6cWYOP/JNJcOAioNwcBMc3AyF26GqyLzory6u/76qCNw19c+zOSCtnzmlIn1A3deB5tek7NbfyHJVma2+S3ZDyV4o2QOle8ztnPPX1r1WhAo4GDB//vwgDkNEgqrvRPj5Ushba36IHtpsfj24yWzDVF0Mu78yl0Ys0HWQGcXNGg5ZI80PbI8Lti42I87bPzELN327wFywQPYoGHa+WcAmiqYTeLsJFKqbgIhI+FUVwRuzYGuO+fPEX8LUu5udDtCcfcVVeAyIs1vp1iVEdyFFwiy17tzFY0BZdS0pCQ0yeb0FBwPhrKgPEJTvNwMGzkozSOCqBGcFH67eRnVlGRN7x9PN4TIv2q02sMaYrQ+tNjxYWbTpIC6PhdMGpZJ0YJV5Yb9tsbkAJHSF/qfCgFOh10lQlmfWvvJe/B/cZH4e+MtiMzNe3TXmOe+hZiIVMXHmTbOYOHPaRIzjsO8d5kW+s7z+4r9hodKGEjIUDBCRCGe1mUUEDy8k6KqGgq31H7jVJdD9WDMA0P1Ys+9tc8b+1FxqnbBnBWzJMQME+9fBvm/MZdnjcMqvzcyEUKWDBZF3mkBptQu3x8BmVbaTiEhYHNgIr15q1geIiYfznoARF7bqJXYXmVMEeqclKHtVOixHjI2EWBuVTjdFlc7GwYC2iE00i3E2KMh5uD9++zG7XVW8ceZEuvVtvn2nFZj/7HK+3F7I/UNHcNmVvc0M1O2fwPalsPNzqCyA9W+aS0ssVvMuf7eh5p39hAxz6kJcKsSnNf7ekWROVyjZbXZdKNxe/7Vwm3kjrLba/Npa9kRI6WlOUUruCSm9ze8N44j1FaKFggEinY09ru6u//DAnh8TC/1ONpdp90BpHnz/AXzxqHkS9/5tsOwJOO23cPwlET13yxtdNwwoqXKR3kJBHBERCaEN78Jb15v1AVL6wCUvm33WW0n1AqSzSEuIpdJZRVGlk360XLw92IrqWgse7XxpbN90vtxeyNe7CrlsXB/IHGYuE24wbyrtXWUGB3YshbxvzYtr7/TVbkPNr10Hgb0VhUAtNjN4kNYPmNr4d+5aKMmFikNQW2Mu7hozQFDrNL+6677aE+ou+OsCAHGpHeKivyUKBohI2yT3qC/stPpfsPRB8wP3ndlmgGDKnXDsDyLyg9Rus5LkiKGsppaiSqeCASIi7cnjgSV/gs/q0m37nwIXzg+4Do03GKB6AdLRpSXa2Vtc1a4FkGtq3ZTX1AKQnnDk86Ux/cysgVW7mkn1j4mFvhPM5fTbgz7OZtlizBoCIWy7GK38CgbYbOadvaFDh7J+/fpGj/nLYrFQW1vbyuGJSNSw2WHsz+D4S2HFc2bxp0Ob4d9XmDUFpv4e+p9WX/ilshCqCht/rSmFnmNh2AXmwaIdpCXGUlZTS7E6CoiItJ+qYnjzOtjyofnzhBvhjHv8rg/QnN3KDJBOwltE0Hunvj14iy3brBaS4o78/3R0nzQsFthVUMnBshq6JUX+1NHOyq9PXG/7QO/Xw79vzWuISAdnj4dJN8GYq8zpAsv/BvtWw78uMOd/GZ6jvMCz8NEfYNzPzdoD8c3PSQuWtAQ7uYXtx6RTZgAA801JREFUe0AVEenUDmyE1y4z5/PGxMEPHzdbpLXR7kJ1EpDOIRytkb3FltMS7GYHgyNIibdzTPckNu8vY9WuQs4a3qM9higB8CsYsGPHDoBGfee9j4mINCsuBabcASddZ2YJrPy7OR8LIDbJvMhPSIP4dLNlVHy6mV3w3RtmVdmP7oalfzGnH4z/xREL2rSF94BaqMwAEZHQW/82vD27vj7AzH9B9glBeWnVDJDOwtsNqT1bIxf5ggH+ZW6O7pvG5v1l3LpgLb9767s2bbt7koMHLxzJyF6pbXodacqvYEDfvn39ekxEpIku3eCsuXDa7Wa/1vi0I08BOONuWPcfM6PgwHpY8QysfA6Gnmu2mep9UlCHV39AVTBARCRkPG74+D74/BHz5/6nwoUvBFwf4HAllS5KqswLo97prSg6JhKFwpIZULetND/rK505LJNXV+RS5XJT5XK3bdsVTn7x0jf876aTfe9dgkMFBEWkfcQlm8vRxDhg1OVwwmWwfYk51WDbYtj4rrn0HAu9TjRPIBMyzF61iRn139u7tGpY9QdUTRMQEQmJykJ44xrY9rH588SbYOof2lQf4HDetoIZXRwkxOr0Vjq2cGYGHK14oNdpQ7rz5e1TKa1u2xhdbg83vPwNOwsq+b/Xv+W5K8eodWgQBfxpecstt3DGGWdwyimnkJzsxwm+iEhrWCwwcIq57N8AX/4Nvv037P3aXFoQY7Fypi0JW+GzZmuqzGGQOdxsVWOPa7K+N91NmQEiIiGw/zv4z1VQvMts2fXDx2HEhUHfTP0UAWUFSMfn7X7UvjUDzIt6fzMDALJS4shKaXru1VpPXDaaHz25jI827uf5z3dw7WR1BQiWgIMBjz32GI8//jg2m40xY8YwZcoUpk6dyqRJk3A4VDFSRIIo8zg4728w5few4W0o3QeVh6CiACoL6r+vKcFieIirLYFdn5uLl8Vm9qzNGm4GCHqOgf6nkp5oRtdVQFBEJLh6Fi4nZv7PobbK7P0982XzMzgEdqutoHQivnpHFe0XDPAGHrznTe1peM8U7jz3WH7/znoe+GATY/ulc0Lv1HYfR0fUpsyAJUuW8O233/LVV1+xYsUK/vznP+NwOJg4caIvOHDiiSditVqDOWYR6aySMs0uAy2pdeIq3c+yD//DpMHpxBzaBPnrzDtTVUVmq8NDm80ihQBDzyVj4F1A+0bXRUQ6FMOAkt1ml4C6JWb/esbuX2f+fuBU+PHfzWKxIaLigdKZhGOaQGErCwgG2xXj+/Ll9gIWrsvnhpe/YeFNk0lJaP/AREcTcDDg4YcfBqCgoIAlS5awePFiFi9ezNatW/n4449ZsmQJd911F0lJSZx66qlMnTqVm266KWgDFxFpIiYWkrIoThiAcfwM8HZAMQwoyzeDAvu/MwMEG9+DTf/ltD3fMsxyPcWVSeEdu4hINKgsrA+yei/+D24GZ1mj1bwzet0Tb8F2xu/BagvpsHKVGSCdSFoYCgjWZwaEJxhgsVj4849H8t3eUnILK/n1f9byzBWqH9BWba6w0rVrVy688EIuvNCc/7Vnzx5fYGDJkiXs3buX9957j//9738KBohIeFgskNzDXAZPMx/b+w28fhXxxbm8GfsH/lp+DRiTzXVFRDo7jxsKd0D+t3VB1LpAatm+5te32iFjMHQ/FrodS23XY/h44yFOP/1KbCEOBED9NAFlBkhnkFp3R7ym1kOV0018bOj/j/kyA8IUDABIjrPzt8tG8+OnlrFow37mL9vJTyeFpvV0ZxH0cqtZWVkMHDiQnTt3smPHDvbt24dhGBiGEexNiYgErudo+PmnVP17FvE7crjD/TTG28VYznkEYnUyKSIdUG2NOU2qdJ/Z6tVVZc7nd1U1/rm6FA59D67K5l8nrX9dcdZhvot/ug4EW33KruFyUbVtYbu8LbfHYG9xFaBggHQOXRwxxFgt1HoMiiqdxMeGvnBma7sJhMqIXin8bsZQ7n5vA/cv3MjoPmkcr/oBAQtKMGDNmjW+bIDPPvuMyspK38X/0KFDmTp1KlOnTg3GpkREgic+DeOSV/jzvb/k1zELsK19DfK+hYtfNO9wHUlVEexbY55c9xgJST2UVSAikWv7UvjfHCjY6v9zYuLNAq6ZwyFrhLl0P86/NrHtKL+0GpfbwG6zkJnc9srlIpHOYrGQlhjLwbIaiiqdZKeGPhhQWBnemgENXTWxH19uL+SD9fnc+Oo3/PeXk0mJV/2AQAQcDHjmmWd8UwEKCwt9F/+9e/fmwgsv9AUAevToEbTBiogEW3ysnX9YzmeNaxAvpz6D7cAGePY0s/3V8B+ZK7mqzCDBvm/M6QV7V0HhtsYvlNgdskdB9gnQ4wTz+2R9/olImJXth0V3wLrXzZ+7ZJrTpewJYI83v8bENfi57rGug8y7/e2Q4t9WuQVmBkOvtARsVgVlpXNIS7BzsKymXYoIVjndVLs85nbD0E3gcBaLhQcuHMl3+0rYXVjFb9/4licvH636AQEIOBjwi1/8AovFQnp6Oj/+8Y99F/+DBg0K5vhERELKYrGQnhDLl6XH8f35Czn2i1vNloT/+SmsfRXK8swCWZ7apk9O6wf2RDi4CSoOwJYPzcWrS6YZGBg8DcZc3SiFVkQkpDxu+PofsPg+qCkBLHDSLJhyJ8SlhHt0QaW2gtIZtWd7QW9WgN1moYsj6LPMA5ISb9YPuPDpZbz/XT4vfLGTi0/s3ebXTYy1daqgQpv+NQ3DoKqqitLSUt8iIhJtUhPs5JdWc5A0jr3yHVjyJ/j8YdiyqH6lxO7Qc4xZayB7tPnV2ybLVWUW19q3GvLWmNMHDm6E8v31AYIVz8JZc2HQGU22X+1y43R7SI5TsEBEgmDfavjvHDObCcyg5LmPmJ9bHVB9W8HQp0qLRIr69oKhDwYUNWgrGEkXysf3TuW3Zx/Lff/dwL11S1uN7ZvG69dPiKj3GUoBBwNefvllPv74YxYvXsyiRYvIyckBIDU1ldNOO82XKTBkyJCgDdZfTz75JH/5y1/Iy8tj2LBhzJs3j8mTJ7e4/tKlS5kzZw7r168nOzub2267jeuvv973+/nz5/PTn/60yfOqqqqIi9PcNJFo16hFjy0GzvgDDJwCu5ZB96FmECC5Z8s1Aezx0PtEc/FyVpoVuHOXwxePmcW4XvoxHHM2nPknM/0WKCiv4fS/fkJpdS1Jjhh6pMaRnRpPj5R4eqbG0SMlnuzUeLJT44izR366bkfXxRFDYoTcFRFporoEPv4TrHwODA84kmHq72Hsz6Ii3T9Qu4vqMgPSlBkgnUf9uUvopwl4sw/C1VbwSH42qR9bD5Tx6ordQXm9r3cVUVpd22lqEAR8RnPppZdy6aWXArBz504WL17MRx99xCeffMJbb73FW2+9hcVioUePHkydOpUpU6Zw1VVXBW3gLVmwYAG33HILTz75JJMmTeKZZ57h7LPPZsOGDfTp06fJ+jt27GDGjBnMmjWLl156iS+++ILZs2fTrVs3fvzjH/vWS05OZvPmzY2eq0CASMfgnf9W1DDVrv9kcwlUbAL0PslcRl8FSx+EFc/A9+/DtsUw4QaY/CvW7K6ktNqcglBWU0vZ/nK+31/elrcjIRQbY+XdGycxNCuyCqhJJ+SqggMb6lv+7a/76qz7/Bh+oRl4TMoK7zjbQa7aCkonlNrwRkaIFUVQ8cDDWSwW5v5oJH/4wbA2v9aY+3KocLoprHAqGNAa/fr145prruGaa64BYP369SxevJgPP/yQDz74gJdeeomXX365XYIBDz/8MNdccw3XXnstAPPmzePDDz/kqaeeYu7cuU3Wf/rpp+nTpw/z5s0D4Nhjj+Xrr7/mr3/9a6NggMViISur4x9QRTqj1FBH1+NT4az7YcxV8MHtZjDg80dgzavE9L0RC/2YemwWvz37WPYVV5FXUsXe4mryiqvIK6mue6wal9sTmvGJX9yGgbPWw5JNBxUMkPbjqoKiXVC0w6xf4r3oL9hq3v0/XPpAOOchGHh6+4+1AcMwOFQN2w5WEBMT2myaXQWqGSCdT/00gc6dGeAVjOzJ9C6xVBRWUVjhpH9GYhBGFfmC/um8bt06X5vBzz//HMDXaSDUnE4nq1at4re//W2jx6dPn86yZcuafc7y5cuZPn16o8fOPPNMnn/+eVwuF3a7+R+tvLycvn374na7OeGEE7jvvvsYNWpUi2OpqamhpqbG97O3noLL5cLlCv1/2kB5xxbJYxQ5kkD24RSHeQApLK8O7b6fOgBmvoZly4fYProLS9EOTl1/J2/EDmKvcTH9Du2nX0JXjD7pMLSrmeLb3NQEZwWU5WEpyzO/lu4zv1YX4Rl4BsZxF6hYYQg88+kO/pqzhXV7ikL+GanP4k7GWY7lwAYo2omleBeWop1QvAtL0Q4s5ftbfJqRkIGROQyj+zCMzOEY3YdBtyFgjYEw7zt/fn8T/1gdA6u/aLdtZifb9X9GgiqSP4uT47znLjUhH9+hsmoAUuJtEfm3CJa0eDu7qeJgaSUuV5dwD6dN/P13anMwYMeOHb6L/48//phDhw4B9QGA7OxsTj/9dKZOndrWTR3VoUOHcLvdZGZmNno8MzOT/Pz8Zp+Tn5/f7Pq1tbUcOnSIHj16MHToUObPn8+IESMoLS3l0UcfZdKkSaxdu5bBg5vvRT537lzuueeeJo8vWrSIhITIj1x7a0CIRKvW7MN5+yyAjfVbd7Fw4Y7QDaoBa587GRj3Af3z3mW0dSujd94POxuv48GGM6YLzpguuGyJ2N2VxLkKiXVXtvy669+k4oPfszXzXHLTT8ZjjdwofrSpKDb3kxVb81m4cG+7bFOfxR2Y4SGjfCN9Cj6nR8lKYjwtp/q6rPFUOLpT7uhBaXxvSuL7UJrQh+qYVDNg6AR2A7t3Abva6Q0c2YdrbYAFh83A1g51uIalGXz2sf6/SGhE4mfxtkLzmLRj30EWLlwY0m2t2W4FrBTszWXhwp0h3VY4uSrM97n0y1XUbG+fm9mhUlnZ8rliQwEHA2bNmsXixYvZtcs86Hgv/tPT0zn11FN9dQKGDh0a6CYCdnj1R8MwjlgRsrn1Gz4+fvx4xo8f7/v9pEmTGD16NI8//jiPPfZYs695++23M2fOHN/PpaWl9O7dm+nTp5OcHLnppS6Xi5ycHKZNm+bLihCJJoHswzWr9/H2ru9ISOvGjBljQjzChs7jskfO5uzSf3Ne7wrSKMNSWQhVBVicFVhxE1dbQlxtSZNnGvZESM7GSOphfu3SAwDrmn+RWHmI43fPZ2ThQjzjZ+MZdRU4ktrxfXVM4yucPLXxEw5VW5g8ZRpJIez+oM/iDqxwG9ZvF2Bd928spXt8DxtdMjG6DoLUfhhp/TBS+0JaP4zUfhCfRqLFQiKQ2eILRw6Px+A3KxcDHt66fjyDszpWK0PpPCL5szhzVxF/37wSIzaBGTPaUOPIDx+8thb27+fE449lxoS+Id1WOC2pXMfG4jx6DxrKjJP7h3s4beJvl7+AgwHPP/88AImJiZx88sm+i/9Ro0aFrRVDRkYGNputSRbAgQMHmtz998rKymp2/ZiYGLp27drsc6xWKyeeeCJbtmxpcSwOhwOHw9HkcbvdHnEfJs2JlnGKtKQ1+3BGslkMtLjK1a77vcdjsLY0gZW1VzLtotPp2nC+q6saqgqhsqBuKTR7gyf3hOQeWOqmEDT5tD3tNlj9EnzxKJbSPdgW343ti3kw7noY9/P6dojSapmpdnqmxrO3uIrvD1YxfkDos7z0WdxBVBXD+rdgzSuwZ0X9444UGP4jOOFyLL3GdphWVnuLq6iu9WCzGPTrlqR9WKJeJH4WZySbx6D2OHcprjILHXdLjo+4v0MwZSSZ54Ml1e6of5/+jj/gYMDdd9/NlClTGD9+fMgLw/grNjaWMWPGkJOTwwUXXOB7PCcnh/POO6/Z50yYMIH33nuv0WOLFi1i7NixLf4RDcNgzZo1jBgxIniDF5Gw8RUQrGjfeXD5pdU4az3EWC30SDmsO4k9DuzZkJzduheNTYBx18GYq2Hd6/D5w2ahsaV/hmWPw9ifwogLIet4sFqD9l46i2HZyewtruK7vSWMH9B8wFjEJ+9b+PJJ+O5NcNfVEbJYYdAZcPylMGSG+X+9g9l2wOxokBEHdps+Z0RCwVtAsKy6llq3h5gQ/l+L5G4CwZRWVyCxsDz0HRoiRcBX8b///e+DOY6gmTNnDldccQVjx45lwoQJPPvss+Tm5nL99dcDZvr+3r17efHFFwG4/vrreeKJJ5gzZw6zZs1i+fLlPP/887z66qu+17znnnsYP348gwcPprS0lMcee4w1a9bwt7/9LSzvUUSCy3twK26H9jwNeStg90qLD/5BPCYWRl0Ox18CG9+Dzx6C/G9h+RPm0iUTBk2DwdPMquNxSuP1x/CeKSzasJ/1+/xLv5NOyOOBLYvM/2c7P6t/vNuxcMJlMPLiDt/ub9tBMxjQPS6659yKRLKGre+Kq1xkdGmakRws0dBNIBi876892jVGisi4pR9EM2fOpKCggHvvvZe8vDyGDx/OwoUL6dvXnN+Sl5dHbm6ub/3+/fuzcOFCbr31Vv72t7+RnZ3NY4891qitYHFxMddddx35+fmkpKQwatQoPv30U0466aR2f38iEnze6HqF042z1kNsTPvcydrt7Y3dNYTta6w2GHY+HHcebF0Mq16A7Z9A+X5Y85K5WGOgzwQzMDB4OnQb2nwXA2F4T7Pmy3d7m9ZxkE7OWQlrX4EvnzKzcQAsdf//xs+GnmM6zf+r7QcrAOgeH+aBiHRgMTYrKfF2SqpcFFc6QxYMMAyjPjOggwcDvDeHvMGPzqDDBQMAZs+ezezZs5v93fz585s8duqpp/LNN9+0+HqPPPIIjzzySLCGJyIRJjnOjtUCHsPMDuie3D5pu7sKzRPmPuntcMZsscDgM8yl1gm5y2BLDnz/IRRsMe9g7vwMcn4PqX3NjIITLoe0jlsoKBDDs80Mim0Hy6l01pIQ2yEPo9IapXmw8jn4+h9QVWQ+5kiBMVfBSddBau/wji8MvJkBmfHKDBAJpbQEMxhQVBm6aY7lNbW43HWF4jv4NIH6zICO2z7xcDqLEZFOz2q1kBJvp6jSPKC2WzCgbppA3/QQZgY0JyYWBpxmLmf+CQq3m4GBLYtgx2dQvAuWPgBLH4QBp8KoK2DouR1ybnNrdU+Oo1uSg4NlNWzMK2NM37RwD0nCpaoIltwPX78AnroTx7R+ZhbACZd16g4e3swABQNEQis1IRYKKkN6J9tbTynObiU+1hay7USC9EQzU1SZASIinUxaYmxdMKD9DgC5vmkCoa9Kf0TpA8xOA+N+Ds4K2Pw+fPMi7FhqTinY/gnEpcLImTDqJ9BjZHjHG2bDs5NZsvkg6/eVKBjQGXk85vSaj+42O32AOc1mwg1mQUBrxz5ZPprymlryS6sBTRMQCTXvNMdQ1jwqrHvtjp4VAJCeaE61KKlyhbwoY6RQMEBEBO88sQqK2jEa7MsMCHcwoKHYRLPbwIgLoWgnrH4Z1rwMpXthxTPm0uN4OPYHEJsENnvdEmvWHrDF1i0xkJAB3Y+FmNAVNQqH4T1TWLL5oOoGdEZ7v4GF/wd7V5k/ZwyBGQ+aWTYCwI66rICuibEkxNSGeTQiHZt3jnso09q950UdvV4AmEUZLRYwDPNv2i2pY52/NEfBABER6qPr7TVPrKTSRUmVua0+6REUDGgorR9MuQNO+y1sWwKrX4RNCyFvrbn4w2o3AwI9jjeX7FGQOQzs0XvLcFhd3YDv9qqjQKdRWQiL74FV/wQMiO1i/r8Yd70ZDBMfb72A/hkJQGV4ByPSwflaI4cyM6CTdBIAsFktpPqmjToVDBAR6Sza44DakHeKQEYXR+QXobPa6osPVhyCb/8NeWvA7QS3y1w8rvrvvY+X7jHnVed/ay6r/2W+nsUG3YaYwYGBU2DYBVF1QeXtKPD9/jJqat04Yjp3WniH5nHDqvnw8X31xQFHzoRp93b49oCB8gYDBnZLBA6FdzAiHZxvmkBFCDMDvJ0EOsE0AaifNtpZ6ga0yxno1VdfTVlZGW+88UZ7bE5EpNXaY95dQ95OAhE1RcAfiRkwofluLU0YBpTshn1r6rMJ8tZAxUE4sMFc1r4Ki++Fib80CxXGRv7fo2dqPKkJdoorXXyfX86IXinhHpIEm9sF338An/7V3GcBMofDjL9A34lhHVqk8xYPHJCRCJpJIxJSaYnKDAi29IRYtrfztNFwapdgwP/+9z8KCwvbY1MiIgFJbYd5dw3VdxKI/IvfgFkskNrHXI77ofmYYUBZnhkY2LPSLFRYshvev83sYDDuejhpFsRHbmE+i8XC8OwUPt96iO/2lSgY0JEU7TL3ydUvQXm++ZgjxZwuM/YasxaGHFH9NIFEqhQMEAkp79364lDWDOiEmQFQXzixo9NRTUSE+oh3e2UG5BZESCeB9maxQHK2uQw5G075Nax5Bb541GxpuORP5vdjrjarsydnh3vEzRrWM9kMBqiIYPRz15pZAKtegK2Lgbp2eInd4ITLYcKN0KVbWIcYLdwegx2H6jIDuiWyfluYByTSwXmzGkN54VqfGRA90/nawts1QZkBIiKdiO+A2k4f/t6aAVE3TSDY7PFw4jUw+irY8DZ8/gjs/w6WPwFfPQPHXwKT55jtDyPIcG8RwX1hLCJYU2a2tjMMwKj7ivnV8JiPYYGUnmaXCGmsOLc+C6Asr/7xAaeZwagh50BM57gTFiz7iquoqfUQa7PSKzWe9eEekEgHl5oQ+hsZRXX1CDpDNwGA9C7m+yxQMEBEpPNIbYdUu4a8wYCI7STQ3mwxZjvD4T+GrR/BZw9D7jKz6OC3C8wsgcn/B44u4R4pYLYXBNiYV4rL7cHenr2IDQO+ehpy/gDuGv+ek9oHuh0L3YdC9+Og21DIOCYqajQEVfkB2PAOrH8Ldi3DlwWQkAGjfgKjr4SuA8M6xGi2tW6KQL+MBGxWS5hHI9LxpSV66x25MAwDiyX4/++8WQfpnWSagDIDREQ6obR27CZQU+tmX0kVAH3Sdce2EYsFBk8zl9wv4ZM/w/YlZsbA2tdg2n1m0CAEJzyt0Tc9gS6OGMpratl2sJyhWcnts+HKQnjnBti80Pw5Js7szmCxAJbGXy0W8HigpsS8C16cC1s+bPBiFrN9ZPfjIGtEffvH5Oyw/32DqqIANr4L69+EnZ/XZU3U6X+qmQUw9FxlAQSBt3jgwG6REbQT6ei85y61HoOymlqS44Kfyu+9KO4smQH1NQPa5+ZQuCkYICJC/TSBkioXHo+BNYR3tfYUVWEYkBBrI6NL5zi4BqTPeLjiLXM+9we/haKd8Oa1sPLvMONB88I1TKxWC8dlJ7NiRyHf7S1tn2DAruXwxrVmy0ZbLEz/k1ls8WgX7hUFcHAjHNgIBzeZXw9shKpCKNphLpv/V79+Qtf6wIB3SesfHQECw4DaGqgugS2LzADA9qVguOvX6TkGhv0Ihp0PKb3CNtSOyFs8cEA3BTlF2kOc3Ua83UaVy01xhSvowQCPx/DdJOk03QTqsi2UGSAi0ol4pwl4DCitdvl+DgVf8cD0hJCk9HUoFotZaHDA6fDl38xWb7u/hGfq7uhOuQsSu4ZlaMOzU+qCASVcOCaEF5UeN3z+MCyZa17Upg+Ei17wPxiS2BUST4Z+J9c/Zhh1LR43mi0e89eZHR4ObDTrEGz72Fy87AkQl2LWHrAnQGwX8/vYBt/HpZpTD7oPha6DwR7XuvdZWwMle8xU/upi84K+ugSqvN83eMxZbq7vqoLaanNx1X31pv431ON4GHaBuaT1a924xG/b64IBygwQaT9pCXaqStwUVTqDXpS4tNqFp+4jNTWhcxQQ9GZbtFcNqXDzOxjw4osvBryRmho/5zSKiIRJbIyVxFgbFU43RZWhDQbsKjBTaTt98cDWsMfB5F/ByEsg5/fw3X/M6u/r34TT7zSnDsSntevd6+E9zWyA9ftC2FGgfD+8dwNs/8T8eeRMOOchcCS17XUtFujS3VwGnFr/uKsaDqw3AwN535pf968HV6W5+P36VrPoY7eh5tL9WPNrXErdlIVdZhu/hl9L99HshXygug+D4ReYWQCqA9Auth30dhJQMECkvaQmxLKvpDokHQW8F8RdHDE4YmxBf/1I5M2AaI9po5HA72DA1VdfHfAdrFAVtBARCaa0xFgqnFUUVjjpnxG6NNfcQm+9AAUDWi2lJ1z4vNmBYOFtsH8dvP9rc7E5ICnLnPOe1MNckr1fe5p3h4NYMM9bRHD9vtKQTC3pVvodMX//lXkH354AM/4KJ1wW2oCHPc5Mo+85pv4xt8u8gHdWNFjKzeCA93tnhXlH/+Bmc0pCdQkUbDWXTf9txfYTzH/DuFSITzWDB3Ep5s9xKfWPxXYx6yXExJlj9n0fDzEOiIlXDYB2Vlrt4mCZefNH0wRE2k99EcHgX7x6L4jTOklbQagPBlQ63VS73MTZO3YQpFXTBAwjiBF7EZEIk5YQy56iqpC26AHILTTvnvXpqhPmgPWdCD9fCqvmm8UFS3ablfWL6+4yN8fmgL4TYOBUGDgFMocd/cLaMMw59Xu+hj0roarIvNN8zJkMyEgkzm6l0ulmR0FFcFKjPR7IW411zWtM2PYcFgzzDvdFL0C3IW1//UDY7K27s24YUJZv1ifw1ijwBgmcFZDSG9L6QmrfBl/7mV8TM6KjNoE04S0e2C3JQXKcHZercxTfEgm3VF/1++D/nyuse83O0kkAzCwIu82Cy21QWOEkOzU+3EMKKb+DAR6P5+griYhEMe98uKIQV5DdVVczoK8yA9rGajMzBE68xpw/XpZnXoSW7qv7Pg9K674Wbje/bv/EXHLugi6ZZlBg4FSzt3yXblBdCvu+MS/8vQGAyoLG2133OqT2IebEazkxcwif7YHv9pYEHgyoLDTn52/JMdsqVh7Cex/CPfpqbGf/2bzjHS0sFjMjI7kHDDy9/nHDMBdrO7ZhlHaz7YC3XoCCnCLtyVsAOSSZAZ2skwCAxWL5f/buPD6K+v7j+Gt3s5s7IQckhDPclyiCIAreoOLd1rtYz18t3tR61AttK2pbxWrVelJvaz3booKoKAKCHMp9QwIk5L6vze78/pjshggJOXazm9338/GYR8js7Ox3Ydid+czn+/mQFOMgr7xWwQARkXDiKRrjz8wAt9sgq6ghGKCaAb4TEWneXW6uOJxhQMFW2L7QvPDetdicj//DW+YCkNjXzDD46bx1mwN6HgW9jzEvdNe8YabNL7iflyyRvBdxHHnbroajerVurIYBuT+a1e63fg57ljdtd+eIxz3gJJbXDWbsmb/HZg+R9ExPu0MJSTsKVDxQJBCSva2R/ZAZ4OkkEEaZAWBOFcgrrw2LugGdEgx44YUXqK6u5uabb+6MlxMRaZckb2aA/z7888prqa13Y7NaQj7aHFQsFug+xFyO/Y2ZSZC1zAwObPvCrD1QmmVu262feeHvWdJHmcEGj1PuhbX/huX/wJG7lksjvoR1X0LZcTDh/2DIGeb8+dI9ZnChNBtKGn6W7jGXnxbj6zECBk+BQVOg77G43LB/3rzO+/sR6aDteSoeKBII3mkCygzwmXDqKNApwYDf//73FBUVKRggIkGtmx+j6x6erICMblHYbUqXDpiISLOK/oATYcpDUL7fnN/efRjEp7X8XHs0HD0dxvySHas+Z/2Hf+EM2wrsWUsga0nrXt8ea05NGDwFBp0G3fo0fdyt+dbStTRmBmiagEhnaiwg6I+aAQ2ZAWEWDPB2FFAwQEQkfPhz3p2Ht61gsk6Yg0p82uGDAD9lsdD7yFM5/X0nf3QWMv+E7SSuf92s/m9zQGLvhqWv+bNbH7N4XmJv86eq3UuIcLkNdhWYgU5NExDpXN38eBfb200gDKcJgDIDRETCSlInfPh7MgP6ql5ASHBEWBmaHs+6vQZL+pzGmaffA9UlEJOiQnkSNvYUV1HnchMZYdX0J5FO5s96R42ZASFSu6aVvOeDYVAzQGcqIiINGr9Q/ZeirU4CoWdURiIA6/aVmm344rorECBhZXu+OUUgMzUWm1VFIkU6U5IfOyF59hl2mQGev1M/tGsMNsoMEBFpkOTHIjweu9VJIOSM7JUIK7JZt7cs0EMJat/vKmJVVvFhtxvduxvHDkjphBGJr3iKB2qKgEjn80wTqHa6qHG6iLLbDvOM1gvXmgGdkSkaLBQMEBFp0O2A6LphGFj80AYt2zNNQDUDQsaojAQA1u0t9dtx05VtyyvnkU828fnGvFZt77BZWX7Pqd4TXAl+Kh4oEjgJURHYrBZcboOSKifpib4JBtS73JRWN2QGhFkwwFtAMAymCSgYICLSwPNlV1fvptrpIsbh24/I8hqnN8qsmgGhY3jPBGxWC4WVdewvqyU9MSrQQwoKBRW1zPl8C28tz8blNrBZLZw2vAexLfy/WrQln8LKOr7fVcxpI9pY0FECRm0FRQLHYrGQFGOnoKKO4qo6n30HeQIBAN2iw6xmgFoLHiwrK6vdL+J2u9v9XBGRzhLrsGG3WXC6DIqrnD4PBnjqBaTEOoiLVCw2VETZbQzqHsfm/eWs21sa9sGA6joXLy3ewXOLdlBRWw/AlBFp3HnGMAb1aPli8e73f+St5dl8t7NQwYAupDEzQMEAkUDoFuPwBgN8xbOvxGg7EWHWCvnAzIBQz/hr9dloZmZmu18k1P8SRSQ0mNF1B3nltRRX1tHLx1Wx1UkgdI3slWAGA/aVhu1FrMtt8MHqvfzls83kltUAMLp3IvdMG86EVtYAmJCZwlvLs1m+s8ifQxUfKq1yUlBhXjQM0DQBkYBI8kPBu6KGfYVbvQBofM9Ol0F5bT0JUaGbGdHqYIBhGP4ch08988wz/PnPfyYnJ4eRI0cyZ84cJk+e3Oz2ixYtYubMmaxfv56MjAzuuOMOrr/++ibbvPfee9x3331s376dgQMH8qc//YkLLrjA329FRDqZNxjgh3li6iQQukZlJPL+qr2HLSJoGAafrstlTXZJi9u53G527Lay9rMt2LpIZ4JvthawIcd8/726RXPHGUM5Z3QG1jZUlx+fmQzAun1lVNTWK4OmC9jekBWQnhBFrP69RAKimx8KIHtS5D2BhnASZbcR47BRVeeiuLJOwQCAL7/80p/j8Jl33nmHW2+9lWeeeYbjjz+ef/zjH5x55pls2LCBvn37HrT9zp07mTZtGtdddx2vv/463377LTNmzKB79+78/Oc/B2Dp0qVcfPHF/OEPf+CCCy7ggw8+4KKLLmLx4sVMmDChs9+iiPhRNz+26GnMDNDds1AzqpfZXnD9vtJmt6muc3HPh2t5f9XeVu7Vyhf7dnV8cJ0oPiqCm04ZxBUT+7eronVGt2h6J0Wzp7iaVbuLOWFIdz+MUnxpe17DFIEe+lwTCRTPBXuJH6YJhGNmAJg3h6rqqimqrKNfCJ+3tToYcOKJJ/pzHD7z+OOPc80113DttdcCMGfOHD777DOeffZZZs+efdD2zz33HH379mXOnDkADB8+nO+//56//OUv3mDAnDlzmDJlCnfffTcAd999N4sWLWLOnDm89dZbnfPGRKRTeIrG+PIL1SOryCyy1VeZASFnRENHgZzSGgoqakmNi2zy+K6CSq5/fSWbcsuxWuCicX1IaKEgk8vlYueOnWQOyMRm812bKH/qFmPn0mP6drjq9PjMZPYU72X5ziIFA7qA7fkNxQNTVS9AJFCSvHPcfTlNwJMZEJ7BgORYB3tLqkO+o0BI5XPV1dWxcuVK7rrrribrp06dypIlSw75nKVLlzJ16tQm604//XReeuklnE4ndrudpUuXcttttx20jSeAcCi1tbXU1tZ6fy8rM1MnnU4nTqfv7zj6imdswTxGkZZ09BhOjDYvvArKa3z+/2B3gXnS3CvRof9jISbSCpkpMewsrOLHrCImD071Pvb5xjx+9946KmrrSY1zMOei0UxoSIdvjtPpZIF7O1NOHYDd3rXSEzt6bI/ra065WLajAKdzgI9GJf6yPa8cgP4p0U3+7XU+IaGgqxzHCZHmuUtRhe/OXQrLzdovidERQf/+/aFbtHmZnF9W3SXff2vHHFLBgIKCAlwuF2lpTYs3paWlkZube8jn5ObmHnL7+vp6CgoK6NmzZ7PbNLdPgNmzZ/Pggw8etH7+/PnExAT/XcEFCxYEeggiHdLeY7hwnxWwsmbDVuZVb/bZeOrdsLfEBljYunopeet9tmsJEklY2YmV979aQflWA5cB/8uysnCfOed/QLzBlUOqKNy4jHkbW7fPcPwsrqoGiGB1VjEf/mcejq6RGBG2ftxlfq4V7FjPvKJ1Bz0ejsewhJ5gP46z91sAG5t37WXevGyf7HPtNvN8KC9rO/PmbfPJPruSqhLz/S9Z+SNROT8EejhtVlVV1artWh0MePXVV1u3w4gIunXrxvDhwzvUgaAjftq54HDdDA61/U/Xt3Wfd999NzNnzvT+XlZWRp8+fZg6dSoJCQmHfxMB4nQ6WbBgAVOmTOlyd6NEoOPH8L7Fu/h83xYSe2Qwbdpon41rV2ElxnffEm23csl5Z6rDSgjam7CTVZ9tpT4+g/EnDOPWf/3Id/uKAbj6uH7cPnUw9la2Zwrnz2LDMHh++9fkldfSc9Sxh82ikMBxutzcvnwhYHDJtJPIOKADSzgfwxI6uspxbN+Qx9s71mCPS2LaNN/UM3v/tVWQX8CxR49m2thePtlnV7J63iZWFmSR1ncg06YOCfRw2syTlX44rQ4GXHnllW0+eR0+fDgPPvigd+69v6WmpmKz2Q66Y5+Xl3fQnX2P9PT0Q24fERFBSkpKi9s0t0+AyMhIIiMjD1pvt9uD+sPEo6uMU6Q57T2GU+PNHvGlNS6f/h/YV2ama/VNjsXhCM/5d6HuyD7mReuKXcWc98wy8spriXXY+POFRzLtiJ7t2me4fhaPz0zmvz/msCq7jElDwrNVY1eQXVKB02UQbbfRJyX+kJ0jwvUYltAS7MdxaoIZiCutdvpsnCXV9QB0T4gO6vfuL97zwWrfng92ltaOuU39igzDaNOyYcMGLrroIh599NF2vYm2cjgcjB079qBUngULFnDccccd8jkTJ048aPv58+czbtw4719ic9s0t08R6br8VUAwq9CsF9BHxQND1siGIoKFlXXkldcyuEccH980qd2BgHDmyQZYvrMowCORluxoKB6YmRrbphaSIuJbSX7ohFRc6ekm0PUuhH3BU5SxKMQLCLY6GOB2u1u1OJ1O9u/fz2effcaFF16IYRjce++9/Pjjj/58H14zZ87kxRdf5OWXX2bjxo3cdtttZGVlcf311wNm+v4VV1zh3f76669n9+7dzJw5k40bN/Lyyy/z0ksvcfvtt3u3ueWWW5g/fz6PPvoomzZt4tFHH+Xzzz/n1ltv7ZT3JCKdJynW84Xq2w//3YXm3K1+KQoGhKpuMQ4G9zArqp97ZAYf3nA8A7urwnp7jM80M/NW7i7G6XIHeDTSnO35nraCOs5FAslz4VpW48TlNnyyz+Jw7ybQ8L49fw+hyucFBG02G927d2fKlClMmTKFgQMH8sgjj/Dcc8/xzDPP+PrlDnLxxRdTWFjIQw89RE5ODqNGjWLevHn069cPgJycHLKysrzbZ2ZmMm/ePG677Tb+/ve/k5GRwd/+9rcmUxuOO+443n77be69917uu+8+Bg4cyDvvvMOECb6ZkyMiwaObJzOg0sedBIoUDAgHL/5qHLsLq5g8OFV1ITpgcI84usXYKalysnZvKUf3TQr0kOQQvMGA7qHbg1ukK+jW0KrWMMypAskdbPFaV++mvNacJtDRfXVV4ZIZ4PduAvfccw+PP/44ixYt8vdLec2YMYMZM2Yc8rG5c+cetO7EE09k1apVLe7zF7/4Bb/4xS98MTwRCWKeCHh5bT1Ol7vVBd8OJ6shM6CvpgmEtH4psfRL0YVRR1mtFsb3T2b+hv0s31mkYECQ8kwTGKAMGJGAirBZiY+KoLymnuKqug5fwHumSlotkBAVntMEPH+HoZ4Z4Juz3BbExsYybNgw9uzZ4++XEhHpsMRoO54buiU+mntnGAZZ3swAXSiKtMZ41Q0IesoMEAkevqx55LkbnhTjCNt6IJ5gQEm176ZeBCO/BwMAoqKiqKmp6YyXEhHpEJvV4o2C+6puQH5FLdVOF1YL9Dqg9ZaING9CQ92AFbuKQvpErKsqqqzzFivLTFUwQCTQPEUEi3wwzbHIUy8gTKcIQNOpF74uKh1MOiUYsHfvXpKSlOInIl2Dr1PDPFMEeiZG44jolI9dkS5veM944iLNtNeNOa3rlyydZ0dDVkCvbtHEOPw+61REDsNT88gXNzKKGwIKyWFaPBDMqReJ0f4pKh1M/H5Wunz5cvbu3cvo0aP9/VIiIj7RzcctetRJQKTtImxWxvYzbyRoqkDw8UwRGKApAiJBwZMZ4NNpAmHaVtDDc3PIF9kWwcqvwYBNmzYxffp0LBYLP/vZz/z5UiIiPuPLeXegTgIi7TVhgOoGBCtP8UC1zxQJDp6Ufl/cyPBkRoZrJwGPxqkXoZsZ0Oq8roceeqhV29XX11NcXMyaNWtYunQpbreb4cOHc+WVV7Z3jCIincrXmQHZRZ5OArqDJtIWEzxFBHcVYRiG2jUGERUPFAkuPi0gWNlYQDCceaeNhvA0gVYHA2bNmtWmL2HDMIv9HHHEEXz44YdERUW1fXQiIgHg88yAQvMOmtoKirTNEb26ERlhpaiyju35FQzqER/oIUkDtRUUCS6eu9jFPkhp91z8KjPAM01AwQD69u3bqmCAzWajW7duDB8+nLPPPptf/OIX2Gy2Dg1SRKQz+TotLEvTBETaxRFh5ei+SSzdUch3O4sUDAgSdfVu7/QnTRMQCQ6+LCCozABTcpyCAV67du1q94tUVFTgdrtJSEho9z5ERDqLL+fdVdTWU1Bhfon0VTBApM3GZyabwYAdRVw+oV+ghyOYAU6X2yDWYSMtITLQwxERGi/cfdJNQJkBQGM3BV91lwpGndLjKjMzk+Tk5M54KRGRDvPlNAFPW8GkGDsJUeFdlVekPQ4sIuiZgiiB1dhJIE51HESChC/rHXmmGiSFeTDA8/6LVDOg4/QFLiJdReMXqg+CAZ7igSkqsiXSHmP6JGG3WcgtqyG7qNonGTa7Cip5eN5GCipqfTDC8JPf8Pem4oEiwcNzF7+kqq7DBVc9afHJ4T5NIAwyAzotGCAi0lU0ZgZ0PLqeVaTigSIdEe2wMbp3N1buLua7nYUdDgaUVjm5eu4KdhRU+miE4WtM36RAD0FEGnjOXZwug8o6F3GR7bvMq65zUe10mfuMDe+MRmUGiIiEIW8woNrZ4ej67oZpAv0UDBBpt/GZyazcXczynUVcOK5Pu/fjchvc9PZqdhRU0qtbNPedPRyr0tzbJS4ygvGZmgIqEiyiHTYiI6zU1rs596nFRNja99lW7zazue02S7sDCqHCk21RVKFggIhI2PBME3C5Dcpq6kmMbn9kvHGagIIBIu01PjOZZ7/aznc7izq0n0c+2cjXW/KJttt4/oqxjMxI9NEIRUQCb1h6PD/sKfVJ5tPQ9PiwrwniCQZU1rmocbqIsodehzwFA0REfiLKbiPabqPa6aK4sq5DwQBlBoh03Lh+SVgtZnAtp7SanonRbd7Heyv38MI3OwH4y4VHKhAgIiHn9WsnsHZPqU/2Naq3PiMToiKwWS243AYlVU7SExUMEBEJC8mxDvaWVHPpC8twRLS/8Up2cUMwQAUERdotPsrOyIxE1u4tZfnOIs47qlebnr86q5i7P1gLwE2nDOKs0T39MUwRkYCKj7Jz3KDUQA8jZFgsFpJiHBRU1FJUWUd6YlSgh+RzCgaIiBzCiIwE9pZUk1Na0+F99eoWTY949eIW6YjxmcntCgbsL6vh16+tpK7ezZQRadx22hA/jlJEREJJcqydgopan3SYCkatDgaccsop7X6R0lLfpKuIiHSWpy8bw4Z9Zbh90BV1cFocVmt4z7sT6ajxmcm8tHgny9tQN6DG6eL/Xv2evPJahqTF8cTFR+n/ooiItJqnqHRRiLYXbHUw4KuvvsJisWAY7TszDvcCFCLStURG2NQ2SySIHNPfrFy/Na+CwopaUuJazrYxDIO731/LD3tK6RZj58Urjgn7ytgiItI23o4C4R4MuOKKK3RBLyIiIgGRHOtgaFo8m/eXs2JXEWeManne/wvf7OCD1XuxWS08c9nR6ughIiJtpmBAg7lz5/pxGCIiIiItG5+ZzOb95Xy3s+VgwJeb85j9ySYA7j97hApqiYhIu3iCAWFfM0BEREQkkMZnJvPast28tnQ3H6ze2+x2FTX1GAZcOr4PV0zs14kjFBGRUKKaASIiIiJB4PhBqSRERVBWU09JlbPFbScNSuXBc0dpiqOIiLSbMgNEREREgkByrINv7zqF/WW1LW5ns1ronxKjQICIiHRIkrdmQMsB6K5KwQARERHpMuKj7MRH2QM9DBERCQPJ3mkCLQehuyproAcgIiIiIiIiEmySYs3gc3GlE8MwAjwa31MwQEREREREROQnUmIjAahzuamscwV4NL6nYICIiIiIiIjIT0Q7bETZzUvm4hDsKBBSwYDi4mKmT59OYmIiiYmJTJ8+nZKSkhafYxgGs2bNIiMjg+joaE466STWr1/fZJuTTjoJi8XSZLnkkkv8+E5EREREREQk0JJDuL1gSAUDLrvsMtasWcOnn37Kp59+ypo1a5g+fXqLz3nsscd4/PHHefrpp1mxYgXp6elMmTKF8vLyJttdd9115OTkeJd//OMf/nwrIiIiIiIiEmDejgIh2F4wZLoJbNy4kU8//ZRly5YxYcIEAF544QUmTpzI5s2bGTp06EHPMQyDOXPmcM899/Czn/0MgH/+85+kpaXx5ptv8utf/9q7bUxMDOnp6Z3zZkRERERERCTgkj3BgIrQCwaETGbA0qVLSUxM9AYCAI499lgSExNZsmTJIZ+zc+dOcnNzmTp1qnddZGQkJ5544kHPeeONN0hNTWXkyJHcfvvtB2UOiIiIiIiISGhJapgmUKzMgOCVm5tLjx49Dlrfo0cPcnNzm30OQFpaWpP1aWlp7N692/v75ZdfTmZmJunp6axbt467776bH374gQULFjQ7ntraWmprG/tRlpWVAeB0OnE6na1/Y53MM7ZgHqNIS3QMSyjQcSxdnY5hCQU6jgWgW7R5yVxQXtNljoXWjjPogwGzZs3iwQcfbHGbFStWAGCxWA56zDCMQ64/0E8f/+lzrrvuOu+fR40axeDBgxk3bhyrVq3i6KOPPuQ+Z8+efchxz58/n5iYmBbHEwxaCnSIdAU6hiUU6DiWrk7HsIQCHcfhrWCvBbDxw6btzHNuDfRwWqWqqqpV2wV9MODGG288bOX+/v378+OPP7J///6DHsvPzz/ozr+HpwZAbm4uPXv29K7Py8tr9jkARx99NHa7na1btzYbDLj77ruZOXOm9/eysjL69OnD1KlTSUhIaPH9BJLT6WTBggVMmTIFu90e6OGItJmOYQkFOo6lq9MxLKFAx7EAFC/PZl72RuJS0pk27ahAD6dVPFnphxP0wYDU1FRSU1MPu93EiRMpLS1l+fLljB8/HoDvvvuO0tJSjjvuuEM+x5P6v2DBAsaMGQNAXV0dixYt4tFHH232tdavX4/T6WwSQPipyMhIIiMjD1pvt9u7xIdJVxmnSHN0DEso0HEsXZ2OYQkFOo7DW/f4aABKq+u7zHHQ2nGGTAHB4cOHc8YZZ3DdddexbNkyli1bxnXXXcfZZ5/dpJPAsGHD+OCDDwBzesCtt97Kww8/zAcffMC6deu48soriYmJ4bLLLgNg+/btPPTQQ3z//ffs2rWLefPmceGFFzJmzBiOP/74gLxXERERERER8b+kWPPCurCy9jBbdj1BnxnQFm+88QY333yztzvAueeey9NPP91km82bN1NaWur9/Y477qC6upoZM2ZQXFzMhAkTmD9/PvHx8QA4HA4WLlzIk08+SUVFBX369OGss87igQcewGazdd6bExERERERkU7laS1YXNU1ige2RUgFA5KTk3n99ddb3MYwjCa/WywWZs2axaxZsw65fZ8+fVi0aJGvhigiIiIiIiJdhCcYUFJVh8ttYLO2XJy+KwmZaQIiIiIiIiIivpQUYwYD3AaUVYdWdoCCASIiIiIiIiKHYLdZiY8yE+qLquoCPBrfUjBAREREREREpBneugGVCgaIiIiIiIiIhAXPVIEiBQNEREREREREwoMnM0DBABEREREREZEw4c0MUM0AERERERERkfCQEqeaASIiIiIiIiJhpbFmgFoLioiIiIiIiISF5Fg7AMWaJiAiIiIiIiISHtRNQERERERERCTMqJuAiIiIiIiISJhJilUBQREREREREZGwktIQDCivraeu3h3g0fiOggEiIiIiIiIizUiIsmO1mH8uCaEiggoGiIiIiIiIiDTDarU0FhFUMEBEREREREQkPCSFYBFBBQNEREREREREWpAcgu0FFQwQERERERERaUFSrB0IrY4CCgaIiIiIiIiItCA5NhKAokpngEfiOwoGiIiIiIiIiLQg2ZMZoAKCIiIiIiIiIuEhSTUDRERERERERMJLckM3AWUGiIiIiIiIiIQJT2vBwgoFA0RERERERETCgqe1YChlBkQEegAiIiIiIiIiwSw9MYrThvcgLSEq0EPxGQUDRERERERERFqQlhDFi786JtDD8ClNExAREREREREJMwoGiIiIiIiIiISZkAoGFBcXM336dBITE0lMTGT69OmUlJS0+Jz333+f008/ndTUVCwWC2vWrDlom9raWm666SZSU1OJjY3l3HPPZc+ePf55EyIiIiIiIiJ+FlLBgMsuu4w1a9bw6aef8umnn7JmzRqmT5/e4nMqKys5/vjjeeSRR5rd5tZbb+WDDz7g7bffZvHixVRUVHD22Wfjcrl8/RZERERERERE/C5kCghu3LiRTz/9lGXLljFhwgQAXnjhBSZOnMjmzZsZOnToIZ/nCRbs2rXrkI+Xlpby0ksv8dprr3HaaacB8Prrr9OnTx8+//xzTj/9dN+/GRERERERERE/CpnMgKVLl5KYmOgNBAAce+yxJCYmsmTJknbvd+XKlTidTqZOnepdl5GRwahRozq0XxEREREREZFACZnMgNzcXHr06HHQ+h49epCbm9uh/TocDpKSkpqsT0tLa3G/tbW11NbWen8vKysDwOl04nQ62z0ef/OMLZjHKNISHcMSCnQcS1enY1hCgY5j6apae8wGfTBg1qxZPPjggy1us2LFCgAsFstBjxmGccj1HXW4/c6ePfuQ454/fz4xMTE+H4+vLViwINBDEOkQHcMSCnQcS1enY1hCgY5j6WqqqqpatV3QBwNuvPFGLrnkkha36d+/Pz/++CP79+8/6LH8/HzS0tLa/frp6enU1dVRXFzcJDsgLy+P4447rtnn3X333cycOdP7e1lZGX369GHq1KkkJCS0ezz+5nQ6WbBgAVOmTMFutwd6OCJtpmNYQoGOY+nqdAxLKNBxLF2VJyv9cII+GJCamkpqaupht5s4cSKlpaUsX76c8ePHA/Ddd99RWlra4kX74YwdOxa73c6CBQu46KKLAMjJyWHdunU89thjzT4vMjKSyMjIg9bb7fYu8WHSVcYp0hwdwxIKdBxLV6djWEKBjmPpalp7vIZMAcHhw4dzxhlncN1117Fs2TKWLVvGddddx9lnn92kk8CwYcP44IMPvL8XFRWxZs0aNmzYAMDmzZtZs2aNtx5AYmIi11xzDb/97W9ZuHAhq1ev5pe//CVHHHGEt7uAiIiIiIiISFcS9JkBbfHGG29w8803eyv/n3vuuTz99NNNttm8eTOlpaXe3z/++GOuuuoq7++eKQkPPPAAs2bNAuCJJ54gIiKCiy66iOrqak499VTmzp2LzWZr9dgMwwBan7IRKE6nk6qqKsrKyhQBlS5Jx7CEAh3H0tXpGJZQoONYuirPNafnGrQ5FuNwW4hP7Nmzhz59+gR6GCIiIiIiIhIGsrOz6d27d7OPKxjQSdxuN/v27SM+Pt4v3Q18xVPoMDs7O6gLHYo0R8ewhAIdx9LV6RiWUKDjWLoqwzAoLy8nIyMDq7X5ygAhNU0gmFmt1hajMsEmISFBH3rSpekYllCg41i6Oh3DEgp0HEtXlJiYeNhtQqaAoIiIiIiIiIi0joIBIiIiIiIiImFGwQBpIjIykgceeIDIyMhAD0WkXXQMSyjQcSxdnY5hCQU6jiXUqYCgiIiIiIiISJhRZoCIiIiIiIhImFEwQERERERERCTMKBggIiIiIiIiEmYUDBAREREREREJMwoGiNczzzxDZmYmUVFRjB07lm+++SbQQxJp1uzZsznmmGOIj4+nR48enH/++WzevLnJNoZhMGvWLDIyMoiOjuakk05i/fr1ARqxSMtmz56NxWLh1ltv9a7TMSzBbu/evfzyl78kJSWFmJgYjjrqKFauXOl9XMewBLv6+nruvfdeMjMziY6OZsCAATz00EO43W7vNjqOJVQpGCAAvPPOO9x6663cc889rF69msmTJ3PmmWeSlZUV6KGJHNKiRYu44YYbWLZsGQsWLKC+vp6pU6dSWVnp3eaxxx7j8ccf5+mnn2bFihWkp6czZcoUysvLAzhykYOtWLGC559/ntGjRzdZr2NYgllxcTHHH388drudTz75hA0bNvDXv/6Vbt26ebfRMSzB7tFHH+W5557j6aefZuPGjTz22GP8+c9/5qmnnvJuo+NYQpYhYhjG+PHjjeuvv77JumHDhhl33XVXgEYk0jZ5eXkGYCxatMgwDMNwu91Genq68cgjj3i3qampMRITE43nnnsuUMMUOUh5ebkxePBgY8GCBcaJJ55o3HLLLYZh6BiW4HfnnXcakyZNavZxHcPSFZx11lnG1Vdf3WTdz372M+OXv/ylYRg6jiW0KTNAqKurY+XKlUydOrXJ+qlTp7JkyZIAjUqkbUpLSwFITk4GYOfOneTm5jY5riMjIznxxBN1XEtQueGGGzjrrLM47bTTmqzXMSzB7uOPP2bcuHFceOGF9OjRgzFjxvDCCy94H9cxLF3BpEmTWLhwIVu2bAHghx9+YPHixUybNg3QcSyhLSLQA5DAKygowOVykZaW1mR9Wloaubm5ARqVSOsZhsHMmTOZNGkSo0aNAvAeu4c6rnfv3t3pYxQ5lLfffptVq1axYsWKgx7TMSzBbseOHTz77LPMnDmT3//+9yxfvpybb76ZyMhIrrjiCh3D0iXceeedlJaWMmzYMGw2Gy6Xiz/96U9ceumlgD6LJbQpGCBeFoulye+GYRy0TiQY3Xjjjfz4448sXrz4oMd0XEuwys7O5pZbbmH+/PlERUU1u52OYQlWbrebcePG8fDDDwMwZswY1q9fz7PPPssVV1zh3U7HsASzd955h9dff50333yTkSNHsmbNGm699VYyMjL41a9+5d1Ox7GEIk0TEFJTU7HZbAdlAeTl5R0UBRUJNjfddBMff/wxX375Jb179/auT09PB9BxLUFr5cqV5OXlMXbsWCIiIoiIiGDRokX87W9/IyIiwnuc6hiWYNWzZ09GjBjRZN3w4cO9xYf1OSxdwe9+9zvuuusuLrnkEo444gimT5/ObbfdxuzZswEdxxLaFAwQHA4HY8eOZcGCBU3WL1iwgOOOOy5AoxJpmWEY3Hjjjbz//vt88cUXZGZmNnk8MzOT9PT0Jsd1XV0dixYt0nEtQeHUU09l7dq1rFmzxruMGzeOyy+/nDVr1jBgwAAdwxLUjj/++INaum7ZsoV+/foB+hyWrqGqqgqrteklkc1m87YW1HEsoUzTBASAmTNnMn36dMaNG8fEiRN5/vnnycrK4vrrrw/00EQO6YYbbuDNN9/ko48+Ij4+3huxT0xMJDo62tuv/eGHH2bw4MEMHjyYhx9+mJiYGC677LIAj14E4uPjvTUuPGJjY0lJSfGu1zEswey2227juOOO4+GHH+aiiy5i+fLlPP/88zz//PMA+hyWLuGcc87hT3/6E3379mXkyJGsXr2axx9/nKuvvhrQcSwhLoCdDCTI/P3vfzf69etnOBwO4+ijj/a2aBMJRsAhl1deecW7jdvtNh544AEjPT3diIyMNE444QRj7dq1gRu0yGEc2FrQMHQMS/D7z3/+Y4waNcqIjIw0hg0bZjz//PNNHtcxLMGurKzMuOWWW4y+ffsaUVFRxoABA4x77rnHqK2t9W6j41hClcUwDCOQwQgRERERERER6VyqGSAiIiIiIiISZhQMEBEREREREQkzCgaIiIiIiIiIhBkFA0RERERERETCjIIBIiIiIiIiImFGwQARERERERGRMKNggIiIiIiIiEiYUTBAREREREREJMwoGCAiIiIiIiISZhQMEBEREREREQkzCgaIiIiIiIiIhBkFA0RERERERETCjIIBIiIiIiIiImFGwQARERERERGRMKNggIiIiIiIiEiYUTBAREREREREJMwoGCAiIiIiIiISZhQMEBEREREREQkzYRsMeOaZZ8jMzCQqKoqxY8fyzTffNLvt4sWLOf7440lJSSE6Opphw4bxxBNPdOJoRURERERERHwnItADCIR33nmHW2+9lWeeeYbjjz+ef/zjH5x55pls2LCBvn37HrR9bGwsN954I6NHjyY2NpbFixfz61//mtjYWP7v//4vAO9AREREREREpP0shmEYgR5EZ5swYQJHH300zz77rHfd8OHDOf/885k9e3ar9vGzn/2M2NhYXnvtNX8NU0RERERERMQvwi4zoK6ujpUrV3LXXXc1WT916lSWLFnSqn2sXr2aJUuW8Mc//rHVr+t2u9m3bx/x8fFYLJY2jVlERERERESkNQzDoLy8nIyMDKzW5isDhF0woKCgAJfLRVpaWpP1aWlp5Obmtvjc3r17k5+fT319PbNmzeLaa69tdtva2lpqa2u9v+/du5cRI0Z0bPAiIiIiIiIirZCdnU3v3r2bfTzsggEeP707bxjGYe/Yf/PNN1RUVLBs2TLuuusuBg0axKWXXnrIbWfPns2DDz540PoXX3yRmJiY9g9cREREREREpBlVVVVce+21xMfHt7hd2NUMqKurIyYmhnfffZcLLrjAu/6WW25hzZo1LFq0qFX7+eMf/8hrr73G5s2bD/n4TzMDysrK6NOnDwUFBSQkJHTsTfiR0+lkwYIFTJkyBbvdHujhiLSZjmEJBTqOpavTMSyhQMexdFVlZWWkpqZSWlra4rVn2GUGOBwOxo4dy4IFC5oEAxYsWMB5553X6v0YhtHkYv+nIiMjiYyMPGi93W7vEh8mXWWcIs3RMSyhQMexdHU6hiUU6DiWrqa1x2vYBQMAZs6cyfTp0xk3bhwTJ07k+eefJysri+uvvx6Au+++m7179/Lqq68C8Pe//52+ffsybNgwABYvXsxf/vIXbrrppoC9BxEREREREZH2CstgwMUXX0xhYSEPPfQQOTk5jBo1innz5tGvXz8AcnJyyMrK8m7vdru5++672blzJxEREQwcOJBHHnmEX//614F6CyIiIiIiIiLtFpbBAIAZM2YwY8aMQz42d+7cJr/fdNNNygIQEREREZGQ53K5cDqdgR6GtMBut2Oz2Tq8n7ANBoiIiIiIiIjJMAxyc3MpKSkJ9FCkFbp160Z6evphO+K1RMEAERERERGRMOcJBPTo0YOYmJgOXWSK/xiGQVVVFXl5eQD07Nmz3ftSMEBERERERCSMuVwubyAgJSUl0MORw4iOjgYgLy+PHj16tHvKgNWXgxIREREREZGuxVMjICYmJsAjkdby/Ft1pL6DggEiIiIiIiKiqQFdiC/+rRQMEBEREREREQkzCgaIiIiISFgzDIOVu4soqwmOdmoFFbWs3VMa6GGISIhTMEBEREREwtqyHUX8/Nml3PPBukAPBYDfvL6Sc55ezI78ikAPRSSoWSyWFpcrr7wy0EMMauomICIiIiJhbVteOQBb95cHeCSmbXlmEGB7fiUDuscFeDQiwSsnJ8f753feeYf777+fzZs3e9d5qu57OJ1O7HZ7p40v2CkzQERERETCWn5FHQBFlXUBHgm43AYl1eZ0hYKK2gCPRiS4paene5fExEQsFov395qaGrp168a//vUvTjrpJKKionj99deZNWsWRx11VJP9zJkzh/79+zdZ98orrzB8+HCioqIYNmwYzzzzTOe9sU6izAARERERCWuFDRfdRZV1uN0GVmvgKqoXV9VhGOafC8oVDJDAMQyDaqcrIK8dbbf5rLPBnXfeyV//+ldeeeUVIiMjef755w/7nBdeeIEHHniAp59+mjFjxrB69Wquu+46YmNj+dWvfuWTcQUDBQNEREREJKwVNmQG1LsNymqcdItxBGwsxQdkJygzQAKp2ulixP2fBeS1Nzx0OjEO31yq3nrrrfzsZz9r03P+8Ic/8Ne//tX7vMzMTDZs2MA//vEPBQNERERERELFgRfdhZV1AQ0GFDYJBgR+2oJIVzdu3Lg2bZ+fn092djbXXHMN1113nXd9fX09iYmJvh5eQCkYICIiIiJh7cAL8MKKOgZ2D9xYDqxbkK/MAAmgaLuNDQ+dHrDX9pXY2Ngmv1utVgzPXJwGTmdjW1G32w2YUwUmTJjQZDubzXfjCgYKBoiIiIhIWDswM6CoMrAX4IWaJiBBwmKx+CxVP5h0796d3NxcDMPw1iVYs2aN9/G0tDR69erFjh07uPzyywM0ys4Rev+6IiIiIiKtVON0UV5T7/090Kn5RQe8vgoIivjeSSedRH5+Po899hi/+MUv+PTTT/nkk09ISEjwbjNr1ixuvvlmEhISOPPMM6mtreX777+nuLiYmTNnBnD0vqXWgiIiIiIStn7aTrAwwMGA4qrG1y+rqae2PjDV3EVC1fDhw3nmmWf4+9//zpFHHsny5cu5/fbbm2xz7bXX8uKLLzJ37lyOOOIITjzxRObOnUtmZmaARu0fygwQERERkbD101T8YJomAGZwIqNbdIBGI9J1XHnllVx55ZXe3/v3739QbQCP66+/nuuvv77Jut///vdNfr/sssu47LLLfD7OYKLMABEREREJWz/NBCioDPA0gZ8EI1Q3QET8RcEAEREREQlbP73YLgzwxfdPgxOBnrYgIqFLwQARERERCVuegoFpCZHAwTUEOpvn9VPjzPGovaCI+IuCASIiIiIStjyZAEPS4ht+D1wwwDAMbwHBIWlxgKYJiIj/KBggIiIiImHLU7BvaEMwoLiqDpf70EXH/K28th6ny3xtT3CioFzTBETEPxQMEBEREZGwVfCTzAC3ASVVgbkAL2rISoh12OjV0EFAmQEi4i8KBoiIiIhI2PLWDEiMIjHaDhzc3q+zeF43Oc5BaryjYXwKBoiIfygYICIiIiJhy1MzICXWQUqco2FdYIIBxZ5gQIzDW0BQwQAR8RcFA0REREQkLLndhvdufGpcJKmx5gV4YWVgLsA9nQSSYw8MBqhmgIj4h4IBIiIiIhKWSqud3mKBybEOkmMDmxngnSYQG+kNBhRX1VHvcgdkPCIS2hQMEBEREZGw5MkASIy244iwNk4TCFDNgKKG8aTEmYEJqwUMozFjQEQOduWVV2KxWA5atm3bFuihtcvcuXPp1q1bp7yWggEiIiIiEpbyG9r2eYIAKQ134wsDNE/fE4RIinFgs1q8mQr5qhsg0qIzzjiDnJycJktmZmab91NXF16BNwUDRERERCQseTIDPCn5KQGeJuApIOgZh+oGiLROZGQk6enpTRabzcaiRYsYP348kZGR9OzZk7vuuov6+nrv80466SRuvPFGZs6cSWpqKlOmTAFgw4YNTJs2jbi4ONLS0pg+fToFBQXe57ndbh599FEGDRpEZGQkffv25U9/+pP38TvvvJMhQ4YQExPDgAEDuO+++3A6nd7Hf/jhB04++WTi4+NJSEhg7NixfP/993z11VdcddVVlJaWejMcZs2a5be/twi/7VlEREREJIh5LvpTvZkB5s9ApeUfWEDQHFckUE5BuTIDJAAMA5xVgXltewxYLB3axd69e5k2bRpXXnklr776Kps2beK6664jKiqqyQX2P//5T37zm9/w7bffYhgGOTk5nHjiiVx33XU8/vjjVFdXc+edd3LRRRfxxRdfAHD33Xfzwgsv8MQTTzBp0iRycnLYtGmTd5/x8fHMnTuXjIwM1q5dy3XXXUd8fDx33HEHAJdffjljxozh2WefxWazsWbNGux2O8cddxxz5szh/vvvZ/PmzQDExcV16O+hJQoGiIiIiEhYKvC2FTTvwHsuwgsC1E3AW0AwzhMMaBiPpglIIDir4OGMwLz27/eBI7bVm//3v/9tctF85plnMmTIEPr06cPTTz+NxWJh2LBh7Nu3jzvvvJP7778fq9VMkh80aBCPPfaY97n3338/Rx99NA8//LB33csvv0yfPn3YsmULPXv25Mknn+Tpp5/mV7/6FQADBw5k0qRJ3u3vvfde75/79+/Pb3/7W9555x1vMCArK4vf/e53DBs2DIDBgwd7t09MTMRisZCent7q999eCgaIiIiISFgqqGhsK3jgz0BnBvx0mkCgChqKdBUnn3wyzz77rPf32NhYbrjhBiZOnIjlgAyD448/noqKCvbs2UPfvn0BGDduXJN9rVy5ki+//PKQd+S3b99OSUkJtbW1nHrqqc2O59///jdz5sxh27ZtVFRUUF9fT0JCgvfxmTNncu211/Laa69x2mmnceGFFzJw4MB2v//2UjBARERERMKSp1Cgt4Bgw0V4SZUTp8uN3dZ55bVqnC6q6lwAJHmCAfENNQM0TUACwR5j3qEP1Gu3QWxsLIMGDWqyzjCMJoEAzzqgyfrY2KYZCG63m3POOYdHH330oNfp2bMnO3bsaHEsy5Yt45JLLuHBBx/k9NNPJzExkbfffpu//vWv3m1mzZrFZZddxv/+9z8++eQTHnjgAd5++20uuOCC1r1hH1EwQERERETCkif93nMHvluMA0tDO7/iyjp6JER12lg8WQF2m4X4yIgm41I3AQkIi6VNqfrBZsSIEbz33ntNggJLliwhPj6eXr16Nfu8o48+mvfee4/+/fsTEXHw5fLgwYOJjo5m4cKFXHvttQc9/u2339KvXz/uuece77rdu3cftN2QIUMYMmQIt912G5deeimvvPIKF1xwAQ6HA5fL1Z633GbqJiAiIiIiYcmTfu+Zm2+zWkiOcTR5rLMcWDzQc+HSWDNA0wRE2mrGjBlkZ2dz0003sWnTJj766CMeeOABZs6c6a0XcCg33HADRUVFXHrppSxfvpwdO3Ywf/58rr76alwuF1FRUdx5553ccccdvPrqq2zfvp1ly5bx0ksvAWYNgqysLN5++222b9/O3/72Nz744APv/qurq7nxxhv56quv2L17N99++y0rVqxg+PDhgFljoKKigoULF1JQUEBVlf+KOCoYICIiIiJhyZN+n9JwB978c2DaC3qLB8Y2jqWxtaAyA0TaqlevXsybN4/ly5dz5JFHcv3113PNNdc0Ke53KBkZGXz77be4XC5OP/10Ro0axS233EJiYqI3iHDffffx29/+lvvvv5/hw4dz8cUXk5eXB8B5553Hbbfdxo033shRRx3FkiVLuO+++7z7t9lsFBYWcsUVVzBkyBAuuugizjzzTB588EEAjjvuOK6//nouvvhiunfv3qS4oa9pmoCIiIiIhJ3qOheVDXP0PXfgobGjQGEndxQoani95Fi7d92BBQ3dbgOrtWOt1kRC0dy5c5t97MQTT2T58uXNPv7VV18dcv3gwYN5//33m32e1WrlnnvuaTIV4ECPPfbYQRfxt956KwAOh4O33nqr2X0DPPvss00KIvqLMgNEREREJOx4LvYdEVbiIhvvj3myBDo7M6Co0gk0zQzwZCm43AbFVZoqICK+pWCAiIiIiIQdb1vBA+boe36HwGUGeDoaANhtVrrFmJkCqhsgIr6mYICIiIiIhB1PW0FP+z4Pz535ogAWEDyQ6gaIiL8oGCAiIiIiYcczDSDlJxffKQGq4O8Zz8HBAM94FAwQEd9SMEBEREREwk5+xcGdBKAxOFDYyRffnpoAzWUG5JcrGCAivqVggIiIiIiEHc+d+NSfBgPiAjNNoPCw0wRUM0D8z+12B3oI0kq++LdSa0ERERERCTueAoEHthWExmkCnd9N4NDTFrrHq2aA+J/D4cBqtbJv3z66d++Ow9G0sKYED8MwqKurIz8/H6vVisPhOPyTmqFggIiIiIiEnQLvNIGfBAMaLsbLa+uprXcRGWHz+1jqXW5KqjytBQ9dM6Czpy1IeLFarWRmZpKTk8O+ffsCPRxphZiYGPr27YvV2v5kfwUDRERERCTsNDdNICHKToTVQr3boKiyjp6J0X4fS3FDIMBigW4xmiYggeFwOOjbty/19fW4XK5AD0daYLPZiIiI6HD2hoIBIiIiIhJ2CrzdBJoGA6xWC8mxDvLKayms6KxggDmWbtF2bNamJ/dqLSidyWKxYLfbsdvtgR6KdAIVEBQRERGRsOJyGxQ1UzMAGlP1CzupiGBzbQUBUhtqBhRW1GEYRqeMR0TCg4IBIiIiIhJWSqrqcDdcVx/yAjzOcwHeOXfjG4sHRh70mKeGQZ3LTVl1faeMR0TCg4IBIiIiIhJWPHf8k2LsRNgOPh3u7I4CniyFpNiDU7Oj7Dbio8yZvfmaKiAiPuTXmgFZWVksXbqUTZs2UVxcTFVVFTExMSQlJTFs2DAmTpxI3759/TkEEREREZEmCso9nQQOvhMPnT9NoKjS00ng0ONJjYukvKaegopaBvWI65QxiUjo80sw4Msvv+S+++5j6dKlh9322GOP5Q9/+AOnnHKKP4YiIiIiItJEQaWnk8Ch+3N3/jSBhuDEIaYsmONxsLOgUkUERcSnfD5N4JFHHuG0005jyZIlGIaB3W5nwIABjBs3jkmTJjFu3DgGDBiA3W7HMAyWLl3KlClTmD17tq+HIiIiIiJyEM9FfrBkBnhe51D1C+CAjgLlCgaIiO/4NDNg4cKF/P73vwfg0ksv5frrr+fYY489ZGsKp9PJsmXLePbZZ3n77be59957mTBhgjIERERERMSvPHfYuzcTDEjp9GkCDQUED5OpUNBJNQxEJDz4NDNgzpw5WCwWnnrqKd544w0mT57cbI9Ku93O5MmTefPNN3nyyScxDIMnnnjCl8MRERERETmIpzBgc2n5KQHqJpAUc7hggDIDRMR3fBoMWL58ORkZGdxwww1tet5NN91ERkYG3333nS+H06JnnnmGzMxMoqKiGDt2LN98802z277//vtMmTKF7t27k5CQwMSJE/nss886bawiIiIi4jueO+zNTRPwBAmKOjkzoNlpAvHmegUDRMSXfBoMKCsrIz09vV3PTU9Pp7y83JfDadY777zDrbfeyj333MPq1auZPHkyZ555JllZWYfc/uuvv2bKlCnMmzePlStXcvLJJ3POOeewevXqThmviIiIiPiO56K6uQKCnnT9qjoXVXX1fh2LYRgUV7VumkC+pgmIiA/5NBiQkZHBpk2bKCkpadPziouL2bRpExkZGb4cTrMef/xxrrnmGq699lqGDx/OnDlz6NOnD88+++wht58zZw533HEHxxxzDIMHD+bhhx9m8ODB/Oc//+mU8YqIiIiI7xRWtlxAMC4yAkeEeZpc6OcL8LKaepwuAzj8NIHOmrYgIuHBp8GAadOmUVlZySWXXEJxcXGrnlNcXMzFF19MdXU1Z511li+Hc0h1dXWsXLmSqVOnNlk/depUlixZ0qp9uN1uysvLSU5O9scQRURERMSPCspbbi1osVg6baqAZ/+xDhtRdtsht+l+QM0AwzD8Oh4RCR8+7SZw77338uabb7JgwQIyMzO56KKLOOGEExg0aBDJyck4HA7q6uooLCxk27ZtfP3117z77ruUl5eTlJTk7UTgTwUFBbhcLtLS0pqsT0tLIzc3t1X7+Otf/0plZSUXXXRRs9vU1tZSW9sYvS0rKwPMLgpOp7MdI+8cnrEF8xhFWqJjWEKBjmPp6oL5GK6qq6fa6QIgMdLa7BiTY+3klNawv7QKZ3qs38aTX1oFQFKso9mxJEZZAKhxuimprCEu0qen8NKMYD6ORVrS2mPWp58kaWlpfPnll5x//vns2rWLl156iZdeeqnF5xiGQb9+/fjwww/bXW+gPSwWy0Hj+Om6Q3nrrbeYNWsWH330ET169Gh2u9mzZ/Pggw8etH7+/PnExMS0fcCdbMGCBYEegkiH6BiWUKDjWLq6YDyGC2sAIrBbDb76fD7Nnf65q6yAlS+Xfk/1dv/djV9bZAFs2JxVzJs3r9ntHFYbdW4L7/13Pt2j/TYcOYRgPI5FWlJVVdWq7XweVhw9ejSbNm3ixRdf5F//+hfLly+npqbmoO2ioqIYP348F110Eddccw2RkYees+Vrqamp2Gy2g7IA8vLyDsoW+Kl33nmHa665hnfffZfTTjutxW3vvvtuZs6c6f29rKyMPn36MHXqVBISEtr/BvzM6XSyYMECpkyZ0mxbSJFgpmNYQoGOY+nqgvkYXp1dAquX0yMhmrPOOqHZ7b6sWsvGkhx6DxzGtMmZfhtP5co9sHkDmRndmTbt6Ga3+8umb8gurmbUuImM7Zfkt/FIo2A+jkVa4slKPxy/5Bg5HA5mzJjBjBkzcLvd7N69m6KiIqqrq4mOjiYpKYn+/ftjtfq0ZEGrxzZ27FgWLFjABRdc4F2/YMECzjvvvGaf99Zbb3H11Vfz1ltvtaq2QWRk5CEDHHa7vUt8mHSVcYo0R8ewhAIdx9LVBeMxXFrjBiA1PqrFsXVPiAKgpLrer++hpMbVuvHER5JdXE1JjSvo/k5DXTAexyItae3x6vcJR1arlczMTDIz/RdRbauZM2cyffp0xo0bx8SJE3n++efJysri+uuvB8y7+nv37uXVV18FzEDAFVdcwZNPPsmxxx7rzSqIjo4mMTExYO9DRERERNrGU5E/NfbQxQM9kmM9Ffz9XECwYf/JhxmP2guKiK+FZfWRiy++mMLCQh566CFycnIYNWoU8+bNo1+/fgDk5OSQlZXl3f4f//gH9fX13HDDDdxwww3e9b/61a+YO3duZw9fRERERNqpoMLTVrDli2/P44X+7iZQ1cpgQHxDR4FytRcUEd/wazAgKyuLpUuXsmnTJoqLi6mqqiImJoakpCSGDRvGxIkT6du3rz+H0CzPNIZD+ekF/ldffeX/AYmIiIiI3xVUeNoKtlyvKtUbDPDvxbentWBrMwM8wQwRkY7ySzDgyy+/5L777mPp0qWH3fbYY4/lD3/4A6eccoo/hiIiIiIi4uW5059ymGCAZ5pAkb+nCXjGc9hggPm4ggEi4is+r+D3yCOPcNppp7FkyRIMw8ButzNgwADGjRvHpEmTGDduHAMGDMBut2MYBkuXLmXKlCnMnj3b10MREREREWnCk2aferhpAg0X5wWVdRiG/1oLemoSJLU6M0A1A0TEN3yaGbBw4UJ+//vfA3DppZdy/fXXc+yxxx6ymqHT6WTZsmU8++yzvP3229x7771MmDBBGQIiIiIi4jeetP/DTRPw1Ayoq3dTUVtPfJR/qskXV7U2M0DTBETEt3yaGTBnzhwsFgtPPfUUb7zxBpMnT262rYHdbmfy5Mm8+eabPPnkkxiGwRNPPOHL4YiIiIiINOG5E3+4AoIxjgii7TagMZXf12qcLqrqzNaCh68Z0JCpoAKCIuIjPg0GLF++nIyMjCYV91vjpptuIiMjg++++86XwxERERER8ap3ub3V+1NiW84MgMaAgb9S8z31Cxw2K3GRLSfseroJVNa5qG4IIIiIdIRPgwFlZWWkp6e367np6emUl5f7cjgiIiIiIl7FVU4MAyyWw9+Jh8Yig4V+Ss0v8tYLsGOxWFrcNj4yAkeEeequqQIi4gs+DQZkZGSwadMmSkpK2vS84uJiNm3aREZGhi+HIyIiIiLi5akXkBzjwGZt+eIbGufx+2uagHc8rchSsFgsdFfdABHxIZ8GA6ZNm0ZlZSWXXHIJxcXFrXpOcXExF198MdXV1Zx11lm+HI6IiIiIiFdBeevqBXh4ggGFfgoGtLZ4oEeqn6ctiEh48Wk3gXvvvZc333yTBQsWkJmZyUUXXcQJJ5zAoEGDSE5OxuFwUFdXR2FhIdu2bePrr7/m3Xffpby8nKSkJG8nAhERERERX2ttJwGP5IaL70J/1Qxo2G9rpiyAOgqIiG/5NBiQlpbGl19+yfnnn8+uXbt46aWXeOmll1p8jmEY9OvXjw8//LDd9QZERERERA6nwNtJoHXBgNSG9H1PEMHXPNMP2hwMUEcBEfEBn04TABg9ejSbNm3i6aefZvLkyURGRmIYxkFLZGQkkydP5umnn2bTpk0ceeSRvh6KiIiIiIiX5456amunCfg5M6DNwYB4zzQBBQNEpON8mhng4XA4mDFjBjNmzMDtdrN7926Kioqorq4mOjqapKQk+vfvj9Xq81iEiIiIiMghFVa0cZqAn2sGtDszQDUDRMQH/BIMOJDVaiUzM5PMzEx/v5SIiIiISLM8d/hbX7DPz60FK9s3nnxlBoiID+jWvIiIiIiEhYI2ZgZ4pgkUVdZhGIbPx9P+zAAFA0Sk4wIWDBg/fjwDBw4M1MuLiIiISJhpLCDYuotvz0V6vdugrLre5+MpbGMwoLunZoAKCIqIDwQsGJCdnc2uXbsC9fIiIiIiEkYMw2hzZkBkhI34SHNWbYGPOwo4XW5Kq51A64MBKQ3dDcpq6qmtd/l0PCISfnxaM+Chhx5q9bYVFRWHfM7999/vyyGJiIiIiFBZ56K23g20PjPAs215bT1FlXUM7O678ZRUmYEAiwW6xbRuPInRdiKsFurdBoUVdWR0i/bdgEQk7Pg0GDBr1iwsFkubnvPggw82+V3BABERERHxNU8RwBiHjRhH60+Bk2Md7Cqs8nkRQU+9gKQYBzZr686frVYLKXEO9pfVUlBRq2CAiHSIX7oJ9OrVC5vN1uI22dnZGIZB3759/TEEEREREREvzxSBtmQFmNv7p51fYcO0g9ZOEfBIjYtkf1mttzOCiEh7+TQYcPTRR7Nq1SoyMjJ46aWXGDlyZLPb9uzZk7y8PHbu3OnLIYiIiIiIHMRzMd/aegEenrZ/njv5vuLtJNDKKQIeai8oIr7i0wKC3333HX/4wx9Ys2YNY8eOZdasWTidTl++hIiIiIhIm3nupHuK8LWWJ5PA19MEitvYScBD7QVFxFd8Ggyw2Wzcc889rFy5kiOPPJKHHnqIMWPGsGzZMl++jIiIiIhImzR2EmjjNIGG4EGBjzMDvG0F2zieVG97QU0TEJGO8UtrwZEjR7J06VJmz57Njh07mDRpErfccguVlZX+eDkRERERkRYVtrGtoIcnM6DIx3P0PdMEUtqYGdBdmQEi4iN+CQYAWK1W7rzzTlatWsUxxxzDU089xahRo/j000/99ZIiIiIiIofkubPf5gKCDZkBnoJ/vlJ4QDeBttA0ARHxFb8FAzyGDRvGkiVLeOyxx8jLy+Oss85i+vTp1NbqA0xEREREOkdBuaebQDszA3xdQLCifcEJBQNExFf8HgwAsFgs3H777axevZqJEyfyxhtvUFJS0hkvLSIiIiLivRPf9poBjcEAl9vw2XiKq9pZQNBTM0CtBUWkg3zaWvBwhgwZwjfffMPf//53Vq5c2ZkvLSIiIiJhrL01A5IaLtbdBpRU1bU5s6DZ8XSwm0BxVR31LjcRtk65tyciIahTgwFgZgnceOONnf2yIiIiIhKmnC43xVVmu+u2Fuyz26x0i7FTUuWkqNI3wQDDMLytBdva6jApxoHVYgYniirr6JEQ1eHxiEh4UihRREREREKa58Lbaml7wT5ovHvvq9T8sup66humHCTF2tv0XJvVQnJDACFfdQNEpAMUDBARERGRkOa5iE+OjcRqtbT5+ak+7ijg2U9cZASREba2jydOdQNEpOMUDBARERGRkFbgrRfQ9qwAaMwM8FVHgfYWD/TwdhQoV2aAiLSfggEiIiIiEtI8d+LbWjzQI8XHd+ILKzoaDPCMR8EAEWk/BQNEREREJKR5Lr5T2pkZ4CkaWOSjaQJF7ewk4OEJahT6KFNBRMKTggEiIiIiEtLy29lW0MPTgaDQV5kBHQ0GxGuagIh0nIIBIiIiIhLSOp4Z4NtgQGNbwY5lBqibgIh0hIIBIiIiIhLSvAUEY9ubGeDbbgIdnyagbgIi0nF+DQacf/75PP3002zYsMGfLyMiIiIiYaq8xklFbX2L23ju6KfGdzAzwEdz9D37SepoNwFlBohIB0T4c+cff/wx//nPfwBIS0vj5JNP5tRTT+XUU0+lX79+/nxpEREREQlx6/eVcvmL31FV5+LMUelcPK4Pxw5IwWq1NNmusOGiOaXdmQHmRXtJlROny43d1rH7aUUdnCbQPd5T0LCObXnlpMZFkhhtx2KxHOaZIiKN/BoMmDNnDl9++SWLFi0iNzeXt956i7fffhuAzMxMTjnlFE499VROOeUUunfv7s+hiIiIiEgI2ZZXwRUvLaekygnAR2v28dGaffRNjuGicb35xdg+pCdGYRiGN52+vTUDusU4sFrAbUBxVR094qM6NPaOThNIjnVgsYDLbXDa418DYLdZSImNpHt8JKlxDlLjIkmNj2RURiKnjehBZIStQ2MWkdDj12DAzTffzM0334xhGKxcuZKFCxeycOFCvv32W3bs2MGOHTt46aWXABg1ahSnnnoqjz/+uD+HJCIiIiJdXHZRFb988TsKK+sY1SuBe88awcc/7OM/a/aRVVTFX+Zv4fEFWzhpaA/OObIndS430P5uAjarhaQYB4WVdRRW+C4Y0N5MBbvNys2nDOY/P+6joLyWspp6nC6D3LIacstqDto+KcbO+WN6cfExfRiWntChsYtI6PBrMMDDYrEwbtw4xo0bx5133kldXR1Lly5l4cKFfPHFFyxdupS1a9eybt06BQMkpBVU1PK3hVs5aWh3ThmWFujhdJqyGiePz99CZmosV0zspzRGERFpt/1lNVz+4nfkltUwuEccr149geRYB8cOSOHes4Yzb20u/1qRzfJdRXyxKY8vNuUBEBcZQZS9/XfHU+IagwEdUV3notrpAiC5nZkKALdNGcJtU4YAUFvvorCijoKKWgoqaskvr6Wgoo7c0ho+37ifnNIaXvl2F698u4sj+3Tj4nF9OOfInsRH2Tv0XkSka+uUYMBPFRYWkp2dTXZ2NllZWRiGEYhhiHSq3NIaLn9xGdvzK/nX99l88duTyOgWHehhHZbn/2d7L+D3llRz9Ssr2Ly/HIDdhVXcd/ZwBQRERIKU0+Wm3tXyuZnNasER0flNqYoq6/jli9+RVVRF3+QYXr92QpNU+xhHBL8Y25tfjO3N9vwK/vV9Nu+t3EtBRS1D0+Nb3rnbDRaLuRyC53U62lHA83yHzUqswzep+5ERNjK6RR/yvGLWuSP5ems+/1qRzecb9/NDdgk/ZJfwh/9uYNoRPblkfB9GZSR2eAyBOiZEpP06JRhQWlrKl19+6Z0msHnzZsC8yIiNjeWMM87wFhYUCUXZRVVc3nDyAlDjdPPop5t48pIxAR5Zy1xug2v+uYItueXcd/YIzjyiZ5uev25vKVfNXUF+eS3dYuyUVDl5+dudVDtd/On8UQcVeBIRkcD6flcR019a7r1z3ZzICCvPTR/LyUN7dNLIzCyzK17+jq15FaQnRPHGtRNIS2g+XX9g9zjuPnM4t08dyg87csiMKITtX0J5DpTthbKchj/vM39W5JmBAHtMwxINjljvn+8prWe73ULK5lNhyNUQk9yu93FgvYDOCIzbrBZOHtqDk4f2oKCilg9W7eWd77PZllfBe6v28N6qPT55HbvNwl8uPJLzjurlk/2JiP/5NRhw9913s3DhQlavXo3b7cYwDOx2O5MmTfIWDzz22GOJiAhIgoJIp9iRX8HlL35HTmkNfZNj+P20YfzmjVV8tGYfV0zsx9h+7TuZ6AyvfLuTrzbnA/CbN1Zx3lEZPHjuSLrFHD6tceHG/dz01mqq6lwMTYvn5auO4dutBdz5/o+8tTyLGqeLP/9iNBEdrMgsIiK+88WmvMMGAgBq6918vSW/04IBVXX1XP3KCtbtLSMl1sHr106gT3JMy09yVsOWT7Gv/Tfjts4HVyvS+w0D6irM5SeOAI6wARuXwOZHYNBpcMQvYOiZZtCgNVz11O5dxyTrWizRQ83X68RMudS4SK47YQDXTs5kVVYx76zI5r8/5lBVd/h/88Nxugy+3JSnYIBIF+LXq/BHH30Ui8VC7969ueSSSzjllFOYPHkyMTGH+fAWCRGbc8u5/MXvKKioZWD3WN649ljSE6O4cGxv/vX9Hh78zwY+nHF8UN4h311YyV/mm1k8Jw/tzqIt+Xy0Zh9LtxfyyM+PaLHmwatLdzHr4/W4DZg8OJW/X340CVF2LjqmD1EOG7e9s4YPVu+lxuniyUvGKK1QRCRIeDLYfnf6UK46vv8ht/nnkt08+ukmb4V+f6utd/Hr11by/e5iEqIiePWa8QzqEXfojV1O2PEVrP03bPpv04v6yERI6AkJGRCfYf45vuH3hAyISze3c1ZCXZUZTPD+uYqFP+7khw2bONO2nOFkwZZPYMsnGPYYLEOnmYGBgadChKNxLPmbYN8ayPkBctZA7jqOqa/mdQdQCvylO/Q8EtJHmz97HglJ/Q8OEFQXQ9EOKNrZ8LNhqSkDawTYIsyf3sXW8NMOaSPgmOvM99vAYrEwtl8yY/sl8/AFR3gLLLbXx2v2cdf7azvtmBAR3/D7LXnDMNizZw8fffQRFRUVVFVVcdJJJ5GUlOTvlxYJqLV7Spn+8neUVDkZ3jOB164Z761ifPvpQ5m3Npcf95Ty3qo9XDiuT4BH25RhGNz13lpqnG6OG5jCy1cew5rsEn777g/syK/k6rnfc9G43tx79ggSDig+5HYbPDxvIy8u3gnAxeP68McLRjXpx3zukRlERVi58c3VfLIul5rXvufZX47tUFEnERHxDU8wYFCPOGIchz5N7JlopuYXlHds7nxr1Lvc3PTmar7ZWkCMw8YrV41n5E/nt7vdkL3MDABs+BCqChsfS+wLR/wcRv0C0kZ26C78kF5VvFK9lmd2/JxMI5tzbUs417qEfs48WPdvWPdv3JHdsA48EUqyYf96cB38d1RniyHbmUimdT/WynzY9rm5eEQmQs/RENsdinc1XPSXtHvcbPkEljwFoy+G426G7kOaPBxhs3Y4S89Tq6Cgwv/HhIj4jl+DAd9++y1ffPEFCxcuZOnSpTz77LM899xzWCwWjjzySG+dAGULSKj5flcRV72ygvLaeo7s041XrxpPYkzjRXOP+ChuOmUQsz/ZxGOfbebMI3oSFxk802XeWZHN0h2FRNmtPPKz0VgsFsb0TWLezZP5y2ebeenbnfzr+z0s3lrAY784kkmDU6muc3HbO2v4dH0uYN5VmnHSwEPOh5w6Mp0XfjWOX7/2PV9uzufquSt44YpxxAbR34GISDjyBAP6tpCC7wlsd7SQXkvyymr4anM+H6zey9IdhTgirLx4xTjG9ksyU+sLtsKub2DXYnOpzGt8ckwqjPqZGQDoM95nafh9GgoWltc4+XZbAV9umsiFm35JRuUGzrUt4WzbMnrUlsCGj7zPcdnjIeNIbBlHQcYY6HkkT6xw8uyinVx3bDr3jDMasgYalrwNUFtqvrefiu8JyQMgKROSM80/x6SA4QJXPbgPXFzgdoKzygySZC2F1a+Zy9Cz4PhboO8En/y9AHSPN4+J/E4IEImI7/j1zHvixIlMnDiRe+65h5qaGr799ltvEcFVq1axevVq/vrXv2K325kwYQKnnnoq999/vz+HJOJ3S7YVcM0/v6fa6WJ8ZjIv/WrcIVv3XHl8f95ansWuwir+/uU27jxjWABGe7Dc0hr+9L+NANw+dSh9UxpPCKPsNu49ewRTR6Zz+7s/kFVUxS9f+o7LJ/Rl/b4y1mSX4LBZ+fOFow87Z/DEId3551XjuXruCpZsL+SKl5fz8pXHkBitNkciIoFQWu2kpMoJHCYYEG+mwfsyJdzlNliTXcyXm/L5cnMe6/eVeR+LsMLcsxM5rvgjePcQF/8AkQkw/BwY9XPIPNFMm/eT+Cg7Z4zqyRmjemIYBhtyxvPV5rO4cWMO9j1LOcqyld1GGmuNTLJqemDdamNwaRxHlCVyRIWdjbnFACTEJ0DvwdB73AF/EQ1TC3J+MKcGJPVvCAD0b31dgp865lrI+g6W/A02/Q82Nyx9jjWDAkPOAGvHMgM8AaKiqjrqXW7VAxLpIjrtNlxUVFSTjgFlZWV89dVXvPHGG/z73//mm2++YfHixQoGSJf2xab9XP/6Kurq3UwenMrz08cR3UzboMgIG/ecNYLrXv2el77ZySXH9KFfSju/6H3EMAzu/XCdN6PhquMzD7nd+MxkPrllMo98sonXlu3mje+yAEiMtvPCFeMYn9m6oogTBqTw+rUT+NXLy1m5u5jLX1zGvWeNwNaBGgr19fVsL4Pvdxd3uDhpekLU4QtUiYiEiKxCMysgNc7RYqZWSqx54VfcwQu/ytp65m/I5ctN+Xy9Nd8biLDhYpQ1i/OSsjgxejsDKn8g4tP8pk+2RZp3/ftPMpde48DefGcBf7FYLIzMSGRkRiI3nDyIkqoJLNtRhGVvKeV7S6nYW0phZR2bcsvZlFvOuysbK/cnxx2iGK/NDulHmIsv9Z0Afd+A/C2w9Cn44W1zasXbyyB1CPSdCBhm1gU0/JnGP1ss0PMoM3DQ7eCpjcmxDqwWcBtmQKBHfOf/W4hI23V6Tm55eTlfffWVN0Ngw4YNnT0EEb8oqapjxhtmIGDKiDSevmwMkREtz4M/bXgPJg9O5ZutBfzpfxt5/opxLW7vb/9bm8PnG/djt1l47OejW7woj42M4A/nj+L0ken8/oO1OCKs/GP6WAZ2b6aoUzPG9E3irf87lukvLWfd3jIueX5ZR98GEMHf1q/o8F4sFvjityeRmRrYII2ISGdozRQB+MmFX2UdPVpo79eSG95cxVeb84mmhqOs25kctZWTY3YwqHYDdlcVVGIucMDF/+SGi/+xAbn4P5xuMQ7OGJXOGaPMYoSGYZBTWsPavaWs21vq/VlX72bigJTOH2D3IXDuU3DyPfDdc7DiZSjYYi6Hs/p1mHe7GagYOs3sotDzKLBYsFktJMdGUlBRS355rYIBIl2E34MBdXV1TaYHrFy5EpfLhdEQeYyIiOCYY47xthoU6ap2FFRS43STlhDJM5cf3aRoXnMsFgv3nT2CM5/8hvkb9vPttgKOH5TaCaM9WHFlHbM+Xg/AjJMGMTQ9vlXPmzQ4lUW/OwnDoN1dEUZmJPKvXx/LvR+uY39Zx+YbGoZBZWUlsbGxHerfnFNaTY3TzaacMgUDRCQs7C4yr7wPFwwwL/wcFFTUUVDRzmCA283w7H9xq+MLjrDuwkZDa7uqhscjE6DPBOh7rHnXOkgv/g/HYrGQ0S2ajG7RnD6yMUDQke9Mn4hPh9NmwaSZsO49qCpoeKBhTN7vT4v5Z2cN7FwE2d9B7lpzWfSo2ZVh6JkwdBo9Yy0UVPh2+oiI+JdfgwFTpkxhyZIl1NTUAOaHn8ViYfTo0d6L/xNOOIG4uLbdSZTwsLekmjkLtlBRW9/idonRdm4/fah3vlqgFDZ8+aUnRrcqEOAxJC2e6cf2Y+6SXTz0nw387+ZJAZlr94f/baCgoo4haXHMOHlgm55rsVg6XJ9pUI943v6/iR3bCeB0Opk3bx7Tpk3Cbm9//YFfv/Y9n63fT0GlTmpEJDxkezIDWjFlLTUusiEY0I4AbuF2jI9u5E73EvB83SX0Mi/6PRf/PYab7fFCkC++M30mKgHGXdW6bU++GyoLYOt82DwPtn0B5fvg+5fg+5d41xLFRkdvkr4ZBXljzOkH3YdCt35+reEgIu3n1/+ZCxcuBGDw4MHei/+TTz6ZlJQApEVJl/P28qwmc+taMqhHHNdOHuDnEbWssOGEKDX2EHMAD+PW0wbz4Zq9bN5fzlvLs5g+sb+PR9eyrzbn8f6qvVgs8OjPRx92ekM4SPFUy1abJBEJE7sLWzdNACAlzlNEsA2fkW43LP8HfP4glvpqKo1InnD9grtuu4OIlP7tGbJ0tthUOOoyc3HWmF0PNv0PtnxKVHkOY6zbIHsbZH/Y+BybA5IHmlMUUoeaLR7TjzC7InSwcKGIdIxfgwFz587l1FNPpVevlquKixzK/jIzo2TKiDROGHzo1Pn5G/bzzdYC8oKglU1hwx3klEMVBDqMbjEOZk4Zwv0freevC7ZwzpEZdItp+37ao6K2nns+WAfA1cdnMqZvUqe8brDzBHUKle4oImHCUzOgX8rhgwGebLxWBwMKt8NHN5gt7oDKjOM5feeF1MT24V4FAromexQMnmIubjfPv/8Ja1Yt4+L+VZyYXNxQi2Ab1FdD/kZzOZAjzgwMpI1qKJo42swIcTQcf85qqCqEqqKGn4Vmh4WqQjPAMOAks2aBAgoi7ebXYMAVV1zhz91LiPNchJ0yrAeXju97yG2q6lx8s7WAgiAIBnhOiFLaOV3hsvF9eX3Zbrbsr2DO51uZde5IXw6vWX/+dBN7S6rpkxzNb6cO6ZTX7ApS2nqiKyLShdXVu9lXUg20LjMg1Zs9dZiAqdsFS5+HhQ9BfY15ATj1D6yIO4s9O75neHxgp/iJj1it0GMY89zgSMjgxF+MMde73VCabQYG8jebAYHcdZC3EeoqzBoE2d817sdihdgeUFsGzqpDv5bHwgchtjsMOs1cBp4CMa3rZiQipk6dwON0OtmxYwdlZWUkJCQwYMCADs3plU7mdkPxTrP/rbseYlLNdLGYVIhOaj4y63ZBeQ4U74aSLChp+Fm2z+yb2/dYs0hQUn8OnETnmaud0kLavfeCLQjmdXtOiFoab0sibFYeOGckl7/4Ha8t282QtHhiI/2brl9UWcery3YDMPuC0cQ4NKfPw5PhocwAEQkH+0qqcRsQZbfSoxUX6J5gQH4LAdO4mhxsr50De5abKwacZFay79aX/O+zAVr1WtI1dI8/xDFhtUJSP3MZPKVxvaseCreagYHcH2H/OrMoYWU+VOQe8PwIiEmB6GTzZ0yyuVQWwI6vzO1/eMtcLFbofQwMmgKDT4P0I5U1IHIYnXLmv3z5ch566CEWLlxIXV3jibXD4WDKlCncd999HHPMMZ0xFGkNw4DyXCjaYkZu8zZC3nozottclNZia/iA9gQIkqG6xLzoL90Dbmfzr7fyFfNnXFpj5eA+x1JSblY1bulOe6pnzmIQZAYUVjbUDOhAIcPjB6UydUQa8zfs5/cfrPXV0A7ronG9mdTMVIxw5U2BrQz8sSUi4m+7D2gr2JpOLI01Aw4RMM3bhHXlXE7a9CJWwwmOeDj9j3D0r7xBf88FY3cFA0KG93uzvBVBdFuEOSWgx3AYfWHj+vL9ULbXvMkUk2x2lWjueKyvg+xlsHWBueRvbMw0+PKPYI+BxN5mccrE3k2XhN6Q2Avs0T545yJdl9+DAS+88AI33HBDk3aCHrW1tfz3v//l008/5dlnn+Waa67x93C8nnnmGf785z+Tk5PDyJEjmTNnDpMnTz7ktjk5Ofz2t79l5cqVbN26lZtvvpk5c+Z02lg7TVURtrcv54y9P2JfU3HobSKizOqw9hgzGltVADWlYLjM3yvzIf8Qz7NGQGIf6NbXXJL6mRf/+Zshaxnk/AAV+2Hjx+YCfGo4+NExgGHfDocNaQ1RYc9iRonTcRBJnfdCPJC8mQHtqBlwoAfPM6cHHK6Lgq90j4/knrNGdMprdSWpygwQkTCSVdT64oEA3b0Xfg3fv7UVsP59WPUa7FmOJ6/NPeBkrOc+Bd36NHl+XpmCAaGmNdkihxWfZi6tEeGAzBPMZeofoCQbtn1uBgZ2LjKnIRRsMZfmJGU27iPzBIjr0f6x+9mS7QW8tTwbt9s4/MbiVwnRdmb/7IhAD8Mn/BoMWLNmDTNmzMDlcnHcccdxxx13MHr0aHr27ElOTg5r167l0UcfZcmSJfzmN79h7NixHHXUUf4cEgDvvPMOt956K8888wzHH388//jHPzjzzDPZsGEDffsePDe9traW7t27c8899/DEE0/4fXwBE9UNy77VRLqqMSxWLMkDIW0E9BjREL0dCcmZB7f6cTnNYi6VBWZwoLLA/D0ywbzo79YX4nu23CLIWQ37VpuBgezvMLK/I7q6mAmWTbBlU7NPGwZsjoKS2liMv/fFEp9uvtaBPxMyzLY2cd198/fUjALvNIGOndj0TIzm+SvG+WJI0gGef8fSaid19W4cEUo1FJHQlVVoZuP1TT58W0HwXPgZ9Cz/ET56B9a9D05zH1gjcA+ayvL6YYy95G6sjoOD5J4LRk0TCB2ewE5xVR31Lnfnt0nu1sdskzjuKjNroDTbzE4t22v+9Cye3+sqzOmvxTth1T8b3sTwxsBA/0kQ0br/D53hT//byPp9ZYEehgBpCZGAggGH9de//hWXy3XIO+n9+/enf//+nHPOOcycOZM5c+bwxBNP8M9//tOfQwLg8ccf55prruHaa68FYM6cOXz22Wc8++yzzJ49+6Dt+/fvz5NPPgnAyy+/7PfxBYzViutnL7L4h+0cf/7V2KPjW/c8m73h4ju9/a9tj4Z+x5kLsKewgiv/8iZjInbx5zN7YqkqgmpPNdmGSrLVRRhVhVjc9XSzVB66Uq2XBYZOg4kzoN/xzaectZPbbVDknSbQOV0AxL8So+3YrBZcboPiqjrSEqICPSQREb9pbCvYirTp2gr6bXmZBY4XGFy/F1Y3rE8ZBGOmw5GX4opKZv+8ec1+3+YrMyDkJMU4sFrAbZg1iXoE8nszwgEpA83lUAzD7EywZwXs/NrMJMhd23guufwfYLFiSx/NaGcK1u9zoedIM1gQ2/kt0utdbrbuN7N2f3f6UOKjVOMpkKLtodOC269H0tdff01CQgKPPfZYi9vNnj2bl19+ma+++sqfwwGgrq6OlStXctdddzVZP3XqVJYsWeKz16mtraW2tjFNqqzMjOQ5nU6czhbmzweYs/8plG514TRsEMBx7i+rYbvRi6qYgdQfc0LzGxoGJz/8H6JqC3jpggx62UqxVORCeW6Tn5bSbNj8P9j8P4y0I3BN+A3GiPPN1jQ+UFRZhydrK85hCep/41Dn+bv3xb9Bcoyd/Io6cksqSY4OnQ9+CX6+PI5FWsOTGdCrW2SLx51l19fY/nsrCaVZJFih2nDAyPOxj52O0edY78X/4Y7hvHKzfXBydISO8xCSHOugoKKOfcWVJAX796Y9HjJPMReAqkIsWUuw7PoG666vsRRuw5qzhkyAzxZ6n2bE9sDoPhQjdRh0H4rRfRhG6lCzzoGf7MivpM7lJtpu5drj+mK1+vamlrRdsH9utXZ8fg0G7N+/nyOPPBLHIdLDDhQZGcnQoUP54Ycf/DkcAAoKCnC5XKSlNZ2PlJaWRm5ubjPParvZs2fz4IMPHrR+/vz5xMS0bj5eIC1YsCCgr7+u2ALYiKivZt68eS1uW0cke4zevLvJxeDEeCAeGAzRmEsPiKvZx4C8z+hT9C0R+9cS8fEMaub9nh3dT2NX6sk4I5rJgjAMIutLia3dT2xtHi5rJPnxI3H+JG0stwoggpgIgwWffdrh9y8d54tj2O62ARY++fJbdnXTHD3pfIH+LJbwYBiwI9/8vNu5dgVV2w7exuaqYeS+t8ks+AKASkcqj1Wfy/vO47jR5SB9XTGs++Sg5zV3DOcUm6+3fuVS8jf48M1IQEU2fG9++uW37E7qit+bNuAk6HsSUelFpFRsIrE6m/iaPcTX7CW2rgBLZR6WyjzY9U2TZ9ZGxFMR2ZPyqJ5URGZQEWX+ucrR3ex00AE/FJrnxakOF59+evD/M5Gfqqo6TGvOBn4NBsTGxpKff6hqcgfLz8/v1Ivkn1bKNQyjVdVzW+vuu+9m5syZ3t/Lysro06cPU6dOJSEhwWev42tOp5MFCxYwZcqUgLZ9rFq1FzatZ0BGd6ZNO7rFbV/PWUHermIGjxrDtCNamqpwLUZVEa7V/8T6/YtEVexnRM6/GZ7/P9yjL8YYNBVL2V4o2YWlaCeWkl1QvAvLTzooGBYbRu9jMAZNxT3oNOg+nO92FcMP35PeLY5p047v+F+AtJsvj+F/5a1k3/ZCBo44kmlHZfhohCKHFyyfxRIeCitqqVu2CIsFLjv3dCJ/kgJrZgPci6U0CwDX0VfhOPUBvnl2DeUFVYwcO44JmU37u7d0DFfW1lO71Awq/OLsqcRFKuU5VLybv5K92wrJHDGaaWN6BXo4HfbT49hZV4ElfwsUbMKSvwlL/mYs+RuxlO8jsr6cyPpyUiqbFiw0bA5IGYT7qOm4j/5Vu7JSd361A7ZsY9zgDKZNC4256uJfnqz0w/Hrp+8RRxzBN998w1dffcVJJ53U7HZffPEFu3bt4sQTT/TncABITU3FZrMdlAWQl5d3ULZAR0RGRhIZefA8OLvd3iVO7AI9zpJqFwAp8ZGHHUePeHNOWnF1/eHHnJgGJ90Bk241qx4v/TuW3B+xrZoLq+Ye+jkWq9kJITnTnHaQvwlL9jLIXobty4cgoTd9Uo7nVGtv6mInd4l/33Dgi2PYM5e1pNoVNv+un63P5X8/5uAyOn5HZ0TPBGacNNCngdZwE+jPYgkPe8vMucjpCVHExRwwz7u2Aj5/AFa8aP6e2BfOexrbgBOxAanxUewoqKK4hc/IQx3DpWVmwd0Yh42kOLV2CyWec7KiqtD63vQex/YkiJ0A/Sc03aC2Agq3QcFWKNza0MVgKxRuw1JfA3kbsM2/G9uK5+HU+2DEBWBtfbbA9gLzxtTQnokh9fcq/tPa48SvwYDp06fz9ddfc+GFF/K3v/2NSy65pMlJoWEYvPXWW9xyyy1YLBamT5/uz+EA4HA4GDt2LAsWLOCCCy7wrl+wYAHnnXee319fWqewwlOM7/CFhTyt/Aor29ACLsIBR14Coy+GXYvhu+fMD/Fu/SB5QMOSaf5M7GNu71G8G7bON5edX0PZHvqVvcNLDnDm/Q3+dRac8DtIH9Wm9yzBJ8XTOisIWlf6W1FlHfd9tI7//Zjjs33+78cchqbFc9oI3wVaRcT3sg/VVnDn1/DRDVBiZgMw7mqY8hBENk6r8xTMLWhjK7m8chUPDFWef9O2HhNdXmQcZBxlLgdyu8yuBlsXwKLHzM4F/74aev7N/P80oHU3QrfuLwdgSFqcb8ctYc+vwYCrrrqKt956iy+++IJf/vKX3HHHHYwYMYL09HRyc3PZsGED+/btwzAMTj31VK688kp/Dsdr5syZTJ8+nXHjxjFx4kSef/55srKyuP766wEzxX/v3r28+uqr3uesWbMGgIqKCvLz81mzZg0Oh4MRI9Sf3R88F/YpsYdPpfIEDNr1xWOxQOZkc2mtpH4w/jpzqauCXYtZ88U7pOZ8RW8KYMOH5jL8HDjxTkhXOldX5Q00VbQh0NQFfbI2h3s/XEdhZR02q4VfTexPv5SOTdtavrOI/63N4S/zN3PKsB4qdiQSxBo7CcRAbTl8Pusn2QBPwYCTDnqe5/u3rZ+R+eVqKxiqOnROFoqsNkjqb54zHnkpLHsGvn0SctbAq+fCwFNhyoMtnivWu9zsyDcLfA7u0cpOXyKt5NdggNVq5eOPP+bWW2/llVdeYe/evezdu7fJNjabjWuuuYbHH38caxvSZTri4osvprCwkIceeoicnBxGjRrFvHnz6NevHwA5OTlkZWU1ec6YMWO8f165ciVvvvkm/fr1Y9euXZ0y5nDj+RJJaUNmQEEgLtgcMTBkKu+u78kbuy7gT8caXO58D9Z/CBv/Yy7DzjaDAj1Ht36/huHz9ofSdqmxnhPd0DypKaqs4/6P1vHfhmyAoWnx/OXCIzmid2KH933eURl8vSWfTbnl/G9tDuccqZoLIsEqq6gKMDjVtRievhjKGzKExl4FU//QJBvgQO298MsrMzsJKDMg9KTGm+dknoCPHCAyDk68w/x/9fVj8P3LsH0hbP/CzFQ95R7o1vegp+0qrKLO5SbGYaNXN02rEd/ye8WWmJgYnn/+ee677z4+/fRTNm3aRFlZGQkJCQwbNowzzzyT3r17+3sYB5kxYwYzZsw45GNz5849aJ3hg/mz0npFnsyAOD9nBviIeVfEgjv9CDj2HDhxo5kOtv4D2PRfcxl6Fpx0J/Q8svGJbjeUZsH+9U2X4p3Q8yg46lIY+TOISW7upcWPAhpo8rNP15nZAAUVZjbAb04cyE2nDiIywjetoLrFOLjuhAE8vmALTyzYwpmj0omwdU7AV0Tapj5vM6/Zn2TypnXmiqRMOPsJGHhyi89r7/dvfsP23VsR8JeupXucWTNAmQEtiOsO0/4ME66HL/5o1rD68W1Y9545bWDYWTB0GsSbRbE9UwQG94hTlp34XKeVb+3Tpw/XXXddZ72cdHGelMPWTRMIfCp3YcOc8lTPeHsMhwtfMTMCvv6z+QG/+X/mMuRMSOjZcOG/AerKD73Tvd+by6d3w9AzzfSyQaeBTYVjOktKXOhlBhRV1vHAx+v5zw/7AHP+4V8uPJLRvbv5/LWunpTJ3CW72FFQyfur93LRuD4+fw0R6YC6Kvj6z/yl4G/YbfW4bZFYJ8+E428Fe9Rhn+4JmOa3d5pAwuFfQ7oWT2ZAKAbRfS5loHmueNyNsOABs1Xhts/N5b+3Qa9xMGwa+cVHAAaD0zRFQHxPvVwk6BiG4b24bs00geDJDDjEeHsMg1+8dEBQ4N+w5Sf9YW0O6D4U0kZBjxGQNtIsWrh1PvzwFuxfBxs+MpfY7nDEhWbxw/TRmkrgZ97iWJV1Pm8/6mt7S6r54383UFbjbHG7TTnl3toA1584gJtPHeyzbICfiouM4DcnDuRP8zby5OdbOe+oDL+9loi0gWHA5nnwyV1QmoUd+MJ1FGOvfZ7E3kNbvZvUdgZMvQUElRkQcjzHRFFlHU6XG7sywg6v11i48r+Qt8m8abRpXuMNob3fcwUwyZFOeflUyL4Ceo/T+Z/4jIIBEnTKaupxusxpGa3JDPBcgFfVuaiqqyfG0fmHdWONg2bG230I/PwFc67YyrlmACBtpLmkDDr03f7uQ8xoce5aWPMWrP0XVOabxWeWPWMGDvpONPeRfoSZjdDMvE5pn5SGmgF19W4qauuJjwrerIz3V+7hk3W5h98Q/2YD/NT0if14cfEO9pZU8/bybH51XH+/v6aItKBoJ3xyJ2z9DABnXAY3Fl3MEvux/NhrSJt21f2AYHxbAqaezIDuCQoGhJqkGAc2qwWX26Coso40ZX+0Xo9h5jL5t1CWY9482vQ/6rZ9xQBrLmS9Ci+9ap7zTbgeRv2iVRk8Ii3x2VWTzdbxuz0Wi4X6+nofjEa6Ms8dhrjICKLshz+uYh02ouxWapxuCivqiEnu3GBAXb2bshrzuPUUnGtW6mA4/U9te4H0I+CMI8xqs9u/gDVvmnd08jaYy4GSMhuDA2kjzYBBfE+z0KG0WbTDRqzDRmWdi8KKuqAOBuwrrQbgrNE9mdpCK78YRwQnDEnttDv0UXYbN54ymPs+XMfTX27jonF9iHYoO0Ck0+1fbwaSf3wXXLVgtcNxN/JNj1/x2ZsbGJkS2+bsJ08AvMbpprLORVxk675/lRkQumxWC8mxDvLLa8kvr1UwoL0SesK4q3GOuZIJ97/PROMH/nJENjHbPzVvEn10gzm1YNxVMO4ac3uRdvDZVZMvCuypSJ9A24oHghlESomNZG9JNfkVtfRJ7twLX894I6wWEqL9GIiw2WHI6eZSXQzbFkLuj+YJXu46qMg1Cw8W7zQLFh4oMgHiekBcWsPP9Ka/R3WDqESIbvgZoRM0j5S4SCqLqiisrKV/amygh9Os3FKzOvcJg1M576heAR5NUxeP68M/Fm1nT3E1/1y6i+tPHBjoIYmEB7fbnHK27BnYuahxfeYJMO0v0H0oOxfvBBraCrZRbGQE0XYb1U4XBeW1rQoGuNyGN+jfQ5kBISk1LtIMBoRQvZ1A2V1YSbErmkWO44m+pOH8b9WrsPwFKNtjTkFd/ASMvAAm/AZ6j+3YCxoGOKuhpsQ8H3QE73mP+IbPrlx27tx5yPUffvghd9xxBwMGDODmm29m5MiRpKWlsX//ftavX89TTz3Fjh07eOyxxzjvvPN8NRzpwjxFZ5JbMUXAIzXeDAYEoojggVMEOm1OeXQSHPELc/GoLGgoSriuIUCwFgq2Qn011JaZS+G21u0/IrppcKD7UDjhd4dseRPqUuIcZBVVkV8e3MWQchqCAemJwdd2yBFh5dbThnD7uz/w3KLtXDahLwlBnGUh0uXVVpg1Z5Y9C0XbzXUWKww/B469AfqM9845zi6qAqBvSvsC6anxDrKLqlsdMC2srMVtgNXSOBVLQkv3+Eg25kCB2gt22Jb9FQAMSos3zzFjkmHSrTDxRtj0H1j2HGQvg7Xvmkvv8WZgwGoDtwvc9eZiuBp+b1jnrILqEjO4UFPS9M8uz/mOxcxoTT/CrFOVfoTZESs2NSB/F+IfPgsG9OvX76B1S5cu5Y477uCKK67g+eefb3KhNHToUE444QR+85vf8H//93/87ne/Y8KECYfcj4QXb/HANpwkeKr4B6KIYKEnkyHQJzWxqWZLmgEnNq4zDKgth4r9jUu55895ZjZBZT7UlDYsZYBhBhAqqs3HAbK/gx//BcffYlaZDqNpB55/V89xGaxyG/p290wMzpTMC8b04rlF29mWV8GL3+xk5pS2zU0WkVYo3QPf/QNW/dP8TAeITISxV8D4/ztkQHd3YSXQvswAMO8CZxdVtzpg6qkXkBwbiU1t0kJSagi35e1sWxraCg7pEdf0AVuEedE/8gLYt9oMCqx7D/YsN5eOsljBcEPBFnNZ917jY/EZDYGB0eb01Pg0c0pqXLoZrDjcjTHDMG9QVRZAVaF57pnUz9yXrQ2XprXlsHclZC83z1PLc83PuOQBkJzZ8HMAJPRu235bw+UMme5efp1c/fDDDxMVFcVTTz3V4h3TJ598krfffpuHH36Yjz76yJ9Dki7Ac3c/tZXTBMxtA9cCrvBwxQMDyWKBqARzSR18+O3dbvMDuqbUjA7XlJof1CteMlveLHoUVr8BU/9gfgGFQTXb7vGBb115ODVOFyVVZheBYJ2fabNamDllCDPeWMVL3+zgyuP6tyn7R0Ra4HbB0qfhiz+Z9QDAPAme8Bs46tIWi8tmNWQG9EtuXzqwJ2Da2mC8t61gvLICQpWnFkS+MgM6bGtDZsCQltoKZoyBn/0DpjxkBgJz15qZAdYIsDT8tNqarrNHN2R/djOzTb1/bvgZGW/eNMr90VxyGn4W7YDyfebSUIS0CZvDnIIan944FbWu0rzxVFnQEAAoOCD74ABWu3mu2n0odB/W+DN5oHnhXbyr8cI/eznkrTcDFgfav+4Q+42Abv3Mz8TuQ83MqD7HmkGM1qopg6ylsPNrc8pVVDezA0QI8Gsw4LvvvmPYsGFERbV8chodHc2wYcNYunSpP4cjXUR7Lq4D2de2MXgRAic2Vqv5RRDdDTggS2fE+WZrw/n3Qmk2/PsqM0Bw5iNmdDiEeTMDgnjuo6deQIzDRkJU8DaJOWNkOiMzEli/r4znFm3n99OGB3pIIl1f4Xb48DfmCTJA3+Pg+Jth8OnmZ3oL3G6D7GKz+Gi/dk4T6B7ftsw8b/FABQNCluffNpAtn0OFJzNgcFrcYbbEvLg98Q7fvXh8GsRPgcFTGtfVlpt1qnJ/NIMOZfvMO/IVuebNI1edeZ5Ymn34/dtjzaxWR6x5oe+sOnRxbIvNDE7UlBy8j259oc8Ec3pEUj8oyTI7phTtMJfiXWaAtGi7uWxbAJ7LzaT+ZlCg7wTzZ/dhjZ+Zzmoz4LDza3PZu9KcauFhiwRnTUh0c/DrWWNFRQX5+fmt2ragoICKigp/Dke6iPak3bf1zoQvFXinNYTwXU6LBUaeD4OnwpK/mcVqdi+Gf5wAY6+CU+41U8NCkCcoVVAZvJkBjfUCojqvbkU7WK0Wbp86lKvmruCfS3ZxzaTMoM1kEAl6bjd8/xIsuN88iXbEwxmzYcwvW521tb+8hrp6NxFWS7unGDVm5rVtmoAyA0JXapyCAb5QV+9mZ4E5jafFzIDOFBkP/Saay0/V1zVMR20IDpQ3TEV1xJkX/bHdISbF/BmbamYneLjdZkHE/M2Qv6lh2WwutWVmIMDmgJ5HNdzZH28GAA7XRcHtNrMYPMGBnB/NwOn+9WagoHgX/Pi2uW1UornP+hozEOD6yfGblGkWX/UsIRAIAD8HAwYNGsT69euZN28e06ZNa3a7efPmsWvXLkaNGuXP4UgX4TmhaFtmQOC+eBrHGwYnNo4YOOkuOOoymH8fbPjQPBld955ZT2Dk+WYaVghJCeAUlNbKLTPv7KV3gQvrk4Z2Z2y/JFbuLuapL7byx/NDO7NExC9Kss3WYp4OAZknwHl/b3OR192F5hSBXknRRNhaziJoTlsv/PKVGRDyPP+2mibQMbsKK6l3G8RHRgRtPaAmIhzQrY+5tJXVan5+devbNBPBMBqCCnmQOrTtF+BWKyT2NpfMExrX15TCnhWQ9Z1ZgHHPSnPdtgWN28T3hMwTGy7+J4dsEW2/BgOuuuoqfvvb33LxxRcza9Ysrr/+emJjG+ekVVVV8dxzz/Hggw9isVi46qqr/Dkc6SI6VkAwENMEgrhmgL906wsX/RN2fgOf3GnO21r4oLl0HwZDp5lLr7GHTVMNdp5jK5hrBuSWmsdgehc4WbBYzOyAS19YxtvLs/n1CQM7vR2oSJdlGLD6dfj0bqgrNzu/THkIjrm2XZ+1WQ3BgPYWD4QDsqcUDJAGygzwDc8UgUFpcUGd9edXFot59/9wGQBtFZUIg04zFwBXPexfC9krzGKD/U+AlIFhURvLr8GAm2++mfnz5/PZZ59xxx13cO+999KvXz9va8GsrCxqa2sxDIOpU6dy8803+3M40kV0JDMgIAUEK9te8DBkZE6GX38NP7xptrTZ9W1jetfixyG2Bww9wwwMDDipaUpYF5HSBU5qckvNzIAucecAmDgwhUmDUlm8rYAb3lzFiJ4JzW5rscC5R/Zi4sCUThyhSBAqy4H/3NJYtKvPBDj/WfOEtZ08xQM7Egxo6zSBvHJzWlOP+K7xeSVt5zkfKq5y4nS5sbcz6yTcedoKDukRJFMEQpktwizEmDEm0CPpdH4NBthsNj7++GP++Mc/8uSTT1JWVsaWLVvYsmWLd5uEhARuvvlm7rvvPmw2mz+HI12Ay21QXNX2YIBnvn4gvni8wYtAtxYMFFsEHH2FuVQXw9bPYfM82Pa5mda16lVziYiG/sdD34nm0mtsl5hvdeBJTb3L3e5UWn/y1gzoAtMEPG4/fSiLtxXw455SftxT2uK2K3cXM/+2E1vcRiTkVJeY1at3LTa7ueSuNStn2xxmnZaJN5rVwTtgtw+DAfnKDJAGSTEObFYLLrdBYUVdl8haC0Zb21I8UKSd/F522m638+CDD3LnnXeyePFiNm3aRHl5OfHx8QwbNoxJkyYRE6MUUTGVVNXhNsw/J8e0PhiQFOPAagG3AcWVdfTopIsiwzC8d4zDappAc6KTYPSF5lJfZxYZ3PyJuZRmmwGCbZ+b29ocZgTWExzoMz4oixB2O+DYKqqqC8q7WfvLPAUEu07mxVF9uvHiFePYlFvW7DaFlXW88u2uoJ6iIeIzVUUHXPwvNi/+MZpu0/sYOPcp6OGbThzetoLt7CQAjW3kymvqqXG6iLK3HKBQAcHQZ7VaSIl1kFdeS0FFrYIB7eSZJhA0xQMlJHVaD6qYmBimTp3K1KlTO+slpQvypNx3i7G36Q6s1WohOTaSgopa8itqOy0YUFnnorbe7HEatpkBzYlwwMBTzOXMx8zer7u+NU92s5aaFWezvzOXb+eYz+kxAkb+DMZf19DeMPBsVgvJsQ4KKuoorAjOYIAnM6CrTBPwOG1EGqeNaL7P776Sal75dhdlNU4MwwjfOZNdQG29i7V7SnG5jcNv3AmsVgsjMxKIcQRvq03ADJpu/Nhs1Zq1lIMu/lMGQf9J0H8y9Dve5/NmswrNSuUdqduREB2B3WbB6TIorKyjV7fmg5KVtfVU1pntuZQZENpS4yLJK69tdcaINFVb72JXQ00PZQaIPwX5t6SEG+9d9na06UuNc1BQUdupdxE9NQpiHTaiHZrm0iyLBdKPMJdjrzeLYBXvhN1LG4MDhdsa+8su+ZsZEDh2htl+JsBSYiO9wYBg43S5vSdboXb3JT7K/Ipyugxq692HveMogXP7uz/ynx/2BXoYTZw4pDv/vHp8oIdxaGX74PtXYOVcczqVR+oQ86K//yRziU/33xBqnBRXOYGOTROwWCykxEaSW1ZDYUVti8EAT1ZAjMNGbKROQUNZ9/hIyFFHgfbaVVCFq6GTQFeaAihdT6d9EpeXl7N9+3YqKipwu93NbnfCCSc0+5iEvo606TPnLZZ3aqE3T/eCZE0RaBuLxWxBmDwAxlxurqvIN6cQfPsk5G+Eb/4Ky56FsVfCcTdBQkbAhpsS54D9jZ0ugkl+eS2GAXabpU1Ta7qCWEeEd4pGWbVTwYAgtnZPCQC9ukUTaQ98XY2dBZUs2pJPdlFV8HSrMAwz/X/FC7Dxv2CYd8iJSzc/546+AhJ7ddpwPJ0EkmMdxEfZO7Sv1HgHuWU1h/3+zdMUgbChjgIds+WAegHKihN/8nswYPXq1dx+++0sWrQIw2g5fdBisVBfX+/vIUkQK+pAZX7PcwKRGaApAj4Q1x2OuhRGX2wWIPz6z5CzBpY9AytehKMuh0m3QlL/Th+aJzgVjHc4PFME0hKisFpD64TBarUQFxlBWU09ZTX19Gi+6YAEkGEY5DbUrXjzugn0S4k9zDP875cvfsfibQW8t2oPt542JLCDqauENW+aUwHyNzau73e82RJw+Dlg69jFeHtk+6B4oIf3wq+85e9fFQ8MH6nx5jlZMH5vdgVbVS9AOolfgwE//PADJ5xwAlVVVRiGQWRkJD169MDaxfuOi/905OI6EFHosG4r6C9WKww/G4adBdsXwtd/MacRrHzF7Eow+iLIPNG8g5bQsPi5K4Fn2orn3zuY5HbRegGtlRBtp6ymnvIaZ6CHIs0oq6mnxmlm/KUFSTrrz8f2YvG2At5ftZdbTh0cuDtrRTvhjQuhcKv5uz3GDHiOvw7SRgZmTA12+6B4oIfnO/tw88Pz1VYwbHT3npMF3/dmV+BpKzhYwQDxM78GAx544AEqKys57rjjeOqppxgzJvx6N0rbFFS2va2gR0oAvniUGeBHFgsMOs1cdn0L3/wFtn8BP7xlLgeKSW0IDvTGGt+TQfvLsaytgMQMc85tXJrZ6aCdFwSeu1iFQZjumFNaDQTPRZivmenL1ZTVKGssWHm6WSRG24NmKsfpI9OJdawjq6iK73cXc0z/AHQqyV4Bb10CVQUQ3xOOv9XMfopK7PyxHEKWLzMD4luXmZenzICw4fk3LlBmQLtsyfNkBqh4oPiXX4MBixcvJioqig8//JDU1MAXAZPgV9jBAoLQuZkBBRXtD15IG/Q/3lz2roRVr0HxLijdA2V7wVllnmxXFUDOD9iAkQAfv9N0H1a7GRSITzN/JmWaxQy79T3sy3szA4LwDofnQixkMwMaiggqMyB4ebJTgqnIVYwjgmlH9OTdlXt4b+Wezg8GbPgI3v8/qK+B9NFw2b983gmgozw1A3wRDOjeysw8TRMIH55sTXUTaLvaehe7G/5/apqA+JtfgwFVVVUMGTJEgQBptY4XEAzMNIH2jFfaoddYc/EwDKguNoMCpXuhbA+u4iz2bVxBr8QIrJX5UJFrbuN2Qtkec/FY+QqcdJfZtaCFObverJMgnCbgqRmQnth8Be+uzFPYrKxamQHBylMvIC3IAlI/H9ubd1fu4X8/5jDr3JGdk7VgGLDkKVhwP2DAkDPg5y9BZPDd3dtdZLYV9GnNgFYWEFQwIPR5MwMUDGizHfmVuNwGCVERKrYpfufXYED//v2pqwu+k2cJXp4Cgu3LDPCkcnf+NAHVDAgQiwViks0l/QgA3E4nq6rnkT5tGlZ7wwV+fS1U5DUsuVCxH358F7KWmCftP7wNZz8BfY895MukeItTBt9JTTDelfWlhGhlBgS7/d5jMLhOWsf3T6ZXt2j2llTz2fpczjvKz5X6XfXwyR3w/UsNA/g/OOMRsAbH1IkDOV1u9pWY/26+KPiY0srMPGUGhA/POVlJlZO6ejeOCNULa60tBxQPVCcB8Te//s+8+OKL2bJlC9u2bfPny0gI8ZxItOdOu/eCrbL2sJ0rfMWbyaCaAcEtIhK69YHeY83ChOOuhqvmwXnPQHQy5G2Al0+Hj2+CqqKDnp4a23iHo7OOrdby3JVND7K7sr6S4MkMUDAgaHmPwSALSFmtFn5+tBkAeG/VXv++WG05vH1pQyDAAqfPhjMfC8pAAMC+kmpcbgNHhNUndx5bG4xXa8Hw0S3ajq2hw00wtuUNZltVPFA6kV+DAXfeeSdHHnkkF110ETt27PDnS0kIqKt3e4uEtedOuycY4HQZnZZS7PmCU82ALshigTGXw00rYcx0c92qV+HpcbD6DTPdt4Hn37fG6aaqth7K95tFDVfOhR/egdqKALwBcLuNMKoZoGkCwWp/kE4TAPjZ0b0BWLw13ztOnyvbB6+cCVvnQ0Q0XPwaTJzR7oKlneHA4oG+aEnqCQYUVdVR73IfchuX26CoUpkB4cJqtTTWcjpMy0lpqjEzIPimF0no8es0gccee4yTTz6Zp556ipEjR3L66aczdOhQYmObT0m7//77/TkkCWKeKQI2q8V7N7AtIiNsxEdFUF5TT35FLYkx/u3bbJ7YqIBglxeTDOc9DWN+Cf+9zcwS+GgGrHkDTrwDqgqJLdzO3xyL6Mc+oh//NdSVN92HIw5G/QyOvhJ6Hd1pFwGFlXU4XQZWS+ieXDfWDFBmQLDaX2Ze4AVbZgBA/9RYxvVL4vvdxXywei/XnzjQdzs3DLPDyUc3Qvk+iO0Ol75jZiAFud0+LB4IkBzrwGIx/0qKquoO2TqwsLIWtwFWi7LpwkVqXCT7y2pVN6CNtuaZNxhUPFA6g1+DAbNmzcJisXjTaj/++ONm574YhoHFYlEwIIx5viySYx3tvlPRPS6S8pp6CitqGdTDvxHVkqo63A03j5NjFAzo8voeC7/+Gpb+Hb56BHZ/C6+e5334XE8eVR2AxexCkDLI7GxQtN3MKlj1KvQYCWN/BUdcaAYa/MhzpzM1LhK7LTTnYzbWDFBmQLDyFhAMwmAAmIUEv99dzHsr9/DrEwZ0fA5uTZnZ3nT5C1C41VyXOhQu/xck9e/weDtDtg/bCoIZxE+OcVBYWUdhxaGDAXlljdMAbT7IRpDg5+0ooPaCrVbjdLG70CzuOdjP57Ei4OdgwBVXXKHCF9JqHSke6JES52BHQaW35Z8/eToJJMXYiQjRC7GwY7PDpFth5AUw/x6zT3hSP0gZxKtb7XxbnMSvzjmN48aNA3vDya5hmIGDVa+a7cTy1ptFxObfByPOg6OvgP6T/JIt4OkkEKpTBOCAzADVDAhKTpfbG8gN1mDAWaN78sDH69maV8G6vWUc0TuxfTvK22gGAH54G5zmyTqOeDjqUjj59xCd5LtB+5mvMwPAvPArrKxr9i6wp8Vcd3XfCRuejDW1F2y9HfmVuA1IjLaHbMafBBe/BgPmzp3rz91LiPHMv0/twImCt4hRJxSr6UixQwlySf3g4tebrFo0dwULC/M42daX4+wHXPRYLObFfv9JcOajZpeCVf+E/etg7b/MpVs/GHEujDjfbI3oo8BAbmk1ELrFA6GxgKAyA4JTfnkthgF2m6VDgVx/SoiyM3VEGv/9MYf3Vu1pWzDAVQ+b/2cGAXZ907g+dSiMvw6OvAQiu14qr6dmQL8UHwYD4h1s3t98R4H8hsyAHkHWdUL8JxAtn7u6rXmN9QJ0Q1U6g1+DASJt4a3M34H59972Rp2QktbYSSA4T4DFtxq7VbSQdRKdBBP+z7xI2LfKzBZY+28o2W32Hl/yFCT0guHnmsGBPhM6VG08J8TbCgLENxQQVM2A4OSZItAjPsonhej85edje/PfH3P4aM1efj9t+OHbnOVtgh/fMbMAyveZ6yw2GDbNbBnYf3JQFwhsiWEYfgkGeOoANFcsTpkB4cdTQFDTBFrPUzxQnQSksygYIEHDk9qf3IGLa+/8tM6YJlDR8UwG6TpS2jL30WIxMwB6jYXTH4Ztn5tTCLZ8BmV74btnzSUuDYadbQYG+k0CW9s+khvbCka3+f10FfHqJhDU9pd66gUE9+fg5EGpdI+PJL+8li8353H6yPSDNyrLgXX/NoMAuWsb18d2h6N/BeOugsTenTdoPymqrKOi1vz/1DvJt9MEAAqayczzfHYqMyB8eNLclRnQelsa2goOUb0A6SQKBkjQ8MXFdWOv407IDFAngbDSOAWljYEmR6xZO2DEeeCsMauPb/wYNs2Div1mX/LvX4KE3jDh12bxwajWpTHnhkHNgITohmkCtfW43IYKjwWZxoBUcB+DETYrF4zpxfNf7+C9lXsagwE1ZbDxP+Z0nh2LgIaqsFY7DJ5iFgIddhZEhM4FrCcrID0hiih7+zOTfio1vuU2cnnl5rGizIDw0d07TUCtBVtrq7etoDIDpHP4LBhgs5lfKMOGDWP9+vVN1rWWxWKhvl53f8KVLwoIenvadkIwoMA7TUAnNuHAc2x1KNBkjzLTjIdNg/o62Pk1bPgQNv0XyvbAgvtg0aNm0cEJvz5sZfLc0uCu4u4LnswAgIraehKj/dsyVNom2DsJHOjnR/fm+a938OXmPEr2bKTbsj/Dpv9BfU3jRn2OhdEXmUVE/dwNJFCyfNxJwONw88M9mQHdD9FpQEJTanwbMurE7CTQ8P9T0wSks/gsGOBpH+j5+dM/t2UfEp4KvHfafVFAsPOmCSgzIDx4gj6FvrrDEeGAwaeZy7S/wNp3zbaG+Rth2TPw3XMw/ByYeCP0GX/Q0w3D8F6IhXJmQGSEjcgIK7X1bsprnAoGBBlPu7iuEAwYmh7P6IxYjtv/FnEvfwDuhguU1CFmAOCIC7tMa8COyPJ0EvBhvQA4fDA+T9MEwo4nM6C02kldvfvwtTrC3La8CgzD7FKVqnNL6SQ+Cwbs3LkTALvdftA6kdbwxcW1J5DQKQUEGwIO+sAOD40FBP1wbNmj4OjpMOaX5jSCpX+H7QvNOgMbPoLex8DEG2DYOd66AmU19VTVuYDgT9HuqPgoO7UVtZRV10PX6d4WFnK7UhHLnB942XknqfZN4AYGngKn3g89j+qyxQDbY7efMwOaC5h6MwM0TSBsJEbbibBaqHcbFFbW0jOE69v4gqeTwOC0eHUSkE7js2BAv379WrVOpDm+qM7vuTCvrHNRXeci2uG7+ZA/VajWgmHFEwwoqqzz39x1iwUGnWou+zfAsr/Dj/+CPSvg3Svh/9u77/CoyrR/4N8zM5lJm9QJKUDoktAEAaUpCBIFLKjoa0NZlf1ZcAV837WvoIug6/r6YmNxXVFBZF2BdRVpAVGa1ABSQgsEQgqpkzqZcn5/nJyThHQ403K+n+uaK+bMOTNPmMeZOfe5n/sOjpayBfreiZzAqwEAEcEBqq779UVhQQbkl9lQWsWOAr4m1x+WCdgrpeU32xbCIjpRLIbgDftU/L/xL+OquDBvj87j3NFJAKjf2vfSTM8yW23wkr3TtUOnExAdakSu1YaLpQwGtEQpHhjL4oHkOczXIZ9QUe1ApV36onAlJ9ehJoOShubuugFsLagtUcHS6+wSgaIKDxRDiu0D3PEhMOswMPp5INgCVBQAe5cAX9yBbp9fgz8bPkVK0HHA5XT/eLzIHChlnFnZUcCn1F2q4rPZKWe2AR+PBLb+LyA6gb534vUuS/Ct6wZ8uz/L26PzCnmZQGeVMwPkgKndKaLkklagclZAiFGPEBNrV2sJOwq0HosHkje4NRgwc+ZMfP/997Bare58GmoH5BNrk0GHkCu4mi8IgpKC6M66AVV2J0prWjMxM0AbDHodIoOlk1LV6ga0RmgH4MaXgOfSgamrpRZnQVEw2grxkCEVb5e/DPw1CfjhOSDjF8BW5rmxeUiY0l6QmQG+pLTO1V6fWyZQZQW+nwUsmQgUngLM8cB9XwH3LEHKtf0BAKv3Z8Hp0latoiq7UwngdFE5GGAy6JWCn5ee+NUWD+TnpdYohSWb6DJBteTMgJ5sK0ge5Nbw7MKFC/H+++9Dr9dj8ODBGDt2LMaNG4eRI0fCZOIHAtWqXX9vuuJ1UtGhRmQVV7q1boDc+SBALygnKtT+WUJNKKqw1ywR8XDkXm8Aetwo3Sb9FStXLkf1gW9xu2kvgsvzgN1/l24AEBoHRPcAorrX/Owh/YzsBhjVPQHwhDA5M6CSwQBfkltTLyAs0ODWJVmtJopA9gHgt2+Bgyuk1p2AFEAb/zoQFAEAuDGpAyKCA5BrteHvv5xGjxjtfPHOq3OFPsoNWW0xoSaUVjmQX1aNLpG1ASK5rWAHdhLQHDkYcJGZAc2qrHbiXJGUtcPMAPIkt57FzJw5E5s3b8bBgwfx66+/YteuXViwYAFMJhNGjBihBAeGDh0KnY4rFrRMzcr8ddctuktBnbaCLPKiHdGhRpzIq+184TX6AOzWD8RyRzQu3tAdz3TLAg6vAk6sA8ovAmU50u3stobHmhOA0Bhp2UGIpeZn9CW/W6QrqT4SOAgLkjMDuEzAl/jMEoG8Y1IA4LdvpSwAWWQ34PaFQLcb6u1uMuhx+9UJ+GLHWcz/8ZiHB+sbEqND3PLZZQk14XR+OTMDSBHD9oKtcuqi1EkgKsSofI8l8gS3BgPeffddAEBBQQE2b96M1NRUpKam4uTJk9i0aRM2b96MV199FWazGaNHj8a4cePwhz/8wZ1DIh8ln1yrcaVCXsOf78ZU7vxythXUomilWrb3v9Rk11yVjY0MrW1RCACVRUDBaemkqPA0UHBK+u+CU0BVMVB6Qbq1RmC4FDwIi6/zMx4IS5DasUX3cM8fd4namgHMDPAlcicBrxQPLDwN/LZSuuUdrt1uCASuuhnodzfQ62apU0cjfn9Dd2Tkl2uyDoVeAKZf390tj20x13z+XnLil8dggGYpywR84HPTlx2vqRfQi0sEyMM8kt8cHR2NKVOmYMqUKQCA8+fPK4GBzZs3IysrC//5z3/www8/MBigUcrJdciVf1GweCAKrWQGMHqrKZaaQJNHawY0QWnpdulV2aBIoNNg6XapikKgKAMoz5duFfLPgvq/l18E7BVAVYl0u3i08UEkXCO1ROw3BQh0X1X22poB2jtx82XyCZ5qwQCnHSg6C5RmA5WF0nytLJQCXBVFtdvKL9bPANAFAD1vkgIAvW8BTC2n2HaKDMaXj12nzrhJIX+GXxqMZ2aAdrU1MyD1aC6WbD/jM/U8RFFEfr4OX+fucWsm6PmiSgBcIkCe55XFznFxcejRowfOnDmDjIwMXLhwAaIoNmhFQ9pRWCbXDFAvM8CdBQTlK8MWdhLQlGgfusKR3VQwoDnBUdKtJaIoBQFKswHrhZqf2VJGgfwz9zBwYZ90W/sS0HcyMGgq0GWE6j3bmRngm5SAVFuCAU47UJzZMGul8BRQfE6q+N8agg7oNloKACTfKgXByOuaWqYnnwh2YDBAc+Tvda353BRFEXP/c0Rpf+k7dDhhLfTIM13TJcIjz0Mk81gwIC0tTckG+OWXX1BRUaGc/CclJWHcuHEYN26cp4ZDPkY+cVcj7V5pY+POzAAVx0v+IzrU/UtQWqOy2qm07nLLem1BkIqtBUUAHZIb36fsInDwa2Dfl0B+OnBguXSL6gEMeggY+ABgjlNlOGZmBvgkuWZAbFNz0OkA8o4AWXuBrD1A1j4g/zjgauZ1DAgGwjpKQaugmuBVUKR0k7cFRQId+ki1L8inyMsELl5SOZ7LBLQrJrTxbJHGnMgrQ2ZhBYwGHd6+e4DaceXL4nQ6kZaWhoEDB0Kvd2+h1PCgAFzfi+9r5FluDQb87W9/U5YCFBYWKif/nTt3xpQpU5QAQHx8vDuHQX5AjhirskzAAwUElfFymYCmeGJutYZ8EhZi1MPsrZ7doTHAiGeA4TOA87uBfV9IRQwLTwGpc4FNfwa6j5au3na9Hoi/WuqIcBnYTcA35VrrZAaIonTFXz7pz9oLXEgDHJUNDzQESp0uLu12EdVdqknhC2cAdFmaWh/OZQLaJb/mJZV22BxOmAxNn1BvOCJ1ABnZIxqTB3X0yPhaYrfboT+/HxMHxCMgIMDbwyFSnVu/RT755JMQBAFRUVG4++67lZP/nj17uvNpyQ/VrsFXYZmAB67e1nYTYGaAlsjpjt6uGZBdIp1gxYYHer+bhSAAna+VbrcsAI6slrIFzu0ETm2SbgBgNANdhgNdR0m3uGaCAy4nUFmsrBePhLS0gZkBPsRWhoSiPRilP4Lrdn4G/JAmreW/lCkMSBgEdBwMdBoCxA2Qrvyzg1C7pLxH1gmYOpwu5Xe2FtSe8KAABOgF2J0iCsqqkRAR1OS+cjDgpj6xnhoekea5/ZKSKIqorKyE1WpVbkSXKlCzgGDNlYmiimo4nC4Y9Op/6ZTHy/Yv2iLPT293E5CvyMZ7u6XbpUyh0hKBQQ8B+SeAExuAM1uBs1ulGgQn1ks3oDY4ENpBKgpXUVD7s7IIQG0NmUE6I/43YCi+r5wIiKN55VhNF9KAo99Ja/CNodJraDTX/Kzzu8sBXNgvZYGc3wMx7zAWiS4gAEBmzWPpDEBsP+mkv+Ng6Rbdiyf+GqJkBtRZJlBYYYcoAjpBnY5B5F8EQUB0iAk51ipcLLU1GQzIK61C2rliAMBNyQwGEHmKW4MBy5Ytw6ZNm5Camor169djw4YNAICIiAiMGTNGyRTo3bu3O4dBPk4URRSquAY/MtgInQC4RKCwototVyLUzGQg/yG/3uXVTlRWOxFkdO/6waYoxQPDmr7C4nWWXtJt+FPSlf7cw1Jg4NLgQHNM4YAxGLrSbNyp34Y7nduAj78ChvwOGPBfbu1g0K6JInDmF2Dr/9ZmbrSRACBLjEaaqycm3HIbdJ2vBeIHAAE+PCfJ7eRgQKXdiXKblMkjLxGwhJqg1zGQp0UxZikY0FwRwU1H8wAAV3cK9067UiKNcmsw4P7778f9998PADhz5gxSU1OxceNG/PTTT1i1ahVWrVoFQRAQHx+PcePGYezYsXjkkUfcOSTyQdYqB+xO6SqgGlcN9DoBUSFG5JdVo6BM/WCAKIpsLahRoSYDjAYdqh1S2msnY7BXxlHbVtBP5p9OL50oxg+oExz4DTi7HaguA4ItNZ0OoqWbXCTOIL0flGXswvefzsMd+u0IyjsMrPlvYMNrwIB7gCGPSvUIqGUuF5D+gxQEyNorbRN0QPJtQEgH6bWwlUo/q8sBW1ntNtEFxPWXrvp3GopDQi/ctuQU4sMDMWkki/+SJMRkQFCAHpV2J/JrgvwXy1gvQOta01FAWSLArAAij/JY5amuXbvisccew2OPPQYAOHz4MFJTU7Fu3TqsXbsWS5cuxbJlyxgM0CA55dpsMiAwQJ0rrdEhJuSXVbulBVypzYFqp6vmeZgZoCWCIMASYsSFkirkl1WjU6S3gwF+ehVWp5dO4Ft5Eh/cZShedP4ebzoexPYJeQg99IXUwWDvEunWcQjQ7QYgqptUhC6ym1SIjunpEkc1cOifwNb3gIIT0jZDoLScY/gM6d+tjbJ+ywEAXsGjBqJDjThfVKm0DJY7CzAYoF1yxsjFJro8VVQ7sPVkPgDWCyDyNK+UoT506JDSZnDr1q0AoHQaIO1xR5s+i9mI9Fz39IOXswJCVQxekP+wmE24UFLl1boBcjeBeI2ciOl0AkJNBlirQpDXZxpCb3gaOLsN2P0pcPQ/NRXs99Q/SG8CIrtKJ7qR3aRq9d1GAzFXeeVvUJ0oAgUnpToLzmrAZQecdum/nXX+uywH2P0PoPSCdJwpHLj2ceC6J6R6DZepXicBojosoSacL6pUivjKJ4AdGAzQLKXlcxPFd7eeyIfN4UKnyCAkxZk9OTQizfNIMCAjI0M5+d+0aRPy86XonxwASEhIwI033ohx45hqqEXySZWahYVqC72pX/W9QGkryKwALZKzQbzZUUCpGeBrBQTdKCwwAKVVDlirHFIBQbkrQVme1NLwYjpQlAEUZkgt7pw2KXsgP73+A0X3ApImSanxCdf4V/ZARSFw+ifgVCpwclPtCX5rhMYBw58GBk9TpdaCHJDS0hyk1lGKCJbbEI7aoDwzA7RLyQxoIohed4mA1zvkEGmMW4MB06dPR2pqKs6ePQug9uQ/KioKo0ePVuoEJCUluXMY5ONqMwPU+6LQ0gfPlchnW0FNi67zRdcb7E6X8uVaSydi5kDp46q0yl7/jtAOwHX/r/42px0oOScFBuQAgVzAsOAEsO096RYaByRNBJJuBbper9QoACBdea8qAUqzAWsWYM2W/tvlBPrcAcT2cevfCwCC6IRwfhdwZgtwMhW4sE9auy8zBErLIfQBgN4o/dTV+W+9ETCYgJ43AVffJ/23SnJrAlJcJkCXstRp7xsOIE/JDOBc0SqLuellAk6XiE3HpOKBKVwiQORxbg0GfPrppwCAkJAQjBo1Sjn5HzRoECN/pJCvsFpUvNIe7cZ+8EobRBYP1CR3zq3WyCu1QRQBo16HqGDtBKTCAgMAANZKR8s76wOk2gFR3etvr7JKHQyO/SC1PSzLAfb8Q7qZwoAuI6WCedYL0om/vaLxx9+yAOg0FLjmEaDvnVL7vSshitLzFZwECk4BBSehv3gct2RshSHtkjHEJAM9xwE9xgJdRniten9tZgDfB6k+ORhfWF6NHrraADozA7QrRg6iN3KBJu1cEQrKq2EONGBotyhPD41I89waDJgzZw7Gjh2LYcOGwWDwSnkC8gNK2n2Iel8UmvvguVLuCF6Q/7AoS1C8kxmQU1IJAIgNN0GnoTZdYUFNZAa0RWAY0H+KdHPYgIyfgWPfA8fWAOV5wPEfGx4TFAmYE4CweOkqfGURcHwtcH63dFv7ItD/bikwkDBIWsLQlMoiIP+klJ2gnPifAgpPNQg86AAYAYiBERB63Aj0qAkAhHe8/L9fRXIwIJZXe+kSdTMDEMZuAgTEmGvmRCOZAetrlgjc2LsDAvR+tGyLqJ1w6xn6n/70J3c+PLUT+W4oIOjWzAA3BC/If0TX/aLrBUq9AI2lZ5vlzIArCQbUZTABvcZLt0n/W1OEcK/U2jAsQTrxN8cDjbWPLM0FDnwF7PsCKDxd29Ugrr8UFOh6PVB0Rjrpz6+5FZwAyi82PR5BLxU8jO4BRPeEM7wrtmWUYfjdTyPA5HuvtbJMQENLVah1LObaYLxoZgFBqs0WsVY5UGV31iu+vLEmGDCeSwSIvEKzl+s/+ugj/OUvf0F2djb69u2L9957D9dff32T+2/ZsgWzZ8/G4cOHkZCQgD/+8Y944oknPDji9ssdBQQtbswMcEfwgvxHtBvnVmv4fVvByxSm1AxoxTKBttLpgM7XSrfWMMcCo2YBI2dKdQj2fQ4c+Q7IOQSs+e8Wjo0HLL2A6J61t6geQGQXaXlDDZfdjqK8NVIbRh9TZnOgvNoJQHtBKWpZ3QK+NhdQaZfqXDAzQLvCgwIQoBdgd4ooKK9Gxwjp8+v0xTKculgOg07A6N4xXh4lkTZpMhiwYsUKzJw5Ex999BFGjhyJv/3tb5gwYQKOHDmCxMTEBvtnZGRg4sSJmD59OpYuXYpt27bhqaeeQkxMDO6++24v/AXtS2G5nHavYgFBc+2XEVEUVa1RUdtNgF9stEhOgZULX3qaHAyI19gVWSUzoFKlzAA1CALQ7XrpNqEQOPhPKVugKEOqV2DpJXUvsPSqDQCY/L9tljwHzSYDQkya/BpBzZBTwgvKq2GteZsMNRkQbORc0SpBEGAJNSG7pAr5pTYlGJB6VCocOKx7tFIXhog8S5PvzO+++y4ee+wxPP744wCA9957D+vWrcPHH3+M+fPnN9h/0aJFSExMxHvvvQcASE5Oxp49e/DOO+8wGKACOZVf1WUCNVkG1U4XrFUOhAep9yGj1AxgNwFNqlscy+USPb5uP9uqzSruZndmBqghOAoY9oR0a+dyrVwiQE2rmxJeaJPeH5kVQHIwoG5HgQ1cIkDkdZoLBlRXV2Pv3r144YUX6m1PSUnB9u3bGz1mx44dSElJqbft5ptvxqeffgq73Y6AgIYnmjabDTZb7Rue1WoFANjtdtjtPnRl6xLy2Dw1RqdLRGGFdHIdbtKp9rx6SFciymwO5BaXI9gQosrjArXdBMID1Rsvqcfdczg0QPpy63SJyC+tQKSHK/rnFEsFBGNCDJqafyFGqbBUSWW1Jv5uT78Xt0VWYTkAoIPZ6JPjI+8KNgAGnQCHS8SFmrqY0SEBnCsaFx0ifVfOLamA3W5HYXk19pwtBACM7hXls/PDl9+LiZrT2jmruWBAfn4+nE4nYmPrRyFjY2ORk5PT6DE5OTmN7u9wOJCfn4/4+PgGx8yfPx9z585tsH39+vUIDm6kIJWP2bBhg0eep9QOiKIBAkTs2JIKvYoXWQMFPcog4PuNW9AjTJ3HdIlAUbkegID9O3/BSWa1+Sx3zuFgvR4VTgGr1mxEnIf/d87IleZfxuF9WJPp2ef2ppP5AgA9zmTlYc2aNd4ejsd46r24LX7Okl4LhzVfU68FtV6IXo8Sl4AL5TXB07JCzhWNqyzSAdBh+75DCM07iF0XBbhEPToGiziwfTMOeHuALfDF92Ki5lRUNNEe+RKaCwbILl1D3tK68sb2b2y77MUXX8Ts2bOV361WKzp37oyUlBSEhal0ZuoGdrsdGzZswPjx4xvNeFDb8dxSYM8ORAQbcdukG1V97C+ydiE/sxg9+12DCf3iVHnMgjIbxJ1bIAjAlNsmQK+h1m7+whNz+P9ObMXp/Ar0HTwM13mwL7LLJeK5XzcCEHHnLWM1VTfAfDIfS07sQ0BIGCZOHO7t4bidp9+L22L390eBzHO4JrkHJo7v5e3hkA/625kdKMkuRVaF9BnZv1dXTJyY5OVRkTcd3XACv17MQFRCV0ycmIwflqcByMOd1/bAxHE9vT28JvnyezFRc+Ss9JZoLhhgsVig1+sbZAHk5eU1uPovi4uLa3R/g8GA6OjoRo8xmUwwmRqukQsICPCLNxNPjbPEJlUZjg41qf58chHBkiqnao9dYpPWykYFGxFoYs0AX+bOOWwJDcTp/AoUqzi3WuNiqQ0OlwidAMRHhmiqJ3NkiBT4KK1y+MV7qFp88TMjr1Ra2pUQGexzYyPfEGMOBLJLkSOtakJcRBDnisbF1nTAKaxwwAkdtp4sAADc3C/BL+aGL74XEzWntfPVZ75JTps2zSPF+IxGIwYPHtwg3WfDhg0YMWJEo8cMHz68wf7r16/HkCFD+MZwhZTigW4oxicXMbqoYj/42k4CDARomUWulq3i3GoNuYp7jNmkqUAAAITVFAEtreK6TW/L1WgRS2o9+fPXJdYUEGT3Hc2r/U5mw47TBaiodiIuLBD9OvputiyRFvhMZsAPP/yAwsJCjzzX7NmzMXXqVAwZMgTDhw/H4sWLkZmZiSeekKpAv/jii8jKysIXX3wBAHjiiSfwwQcfYPbs2Zg+fTp27NiBTz/9FMuXL/fIeNsz+eRazbaCMrn1X4GK/eDzy+XgBb/YaFltH2315lZrZJdIl9niaq6waInSTcDm8EoXB6qVa5XmfRyDAdQEyyUB8w6cK5onf8/LL7UpXQRu6tNB1dbPRNR2PhMM8KT/+q//QkFBAV5//XVkZ2ejX79+WLNmDbp06QIAyM7ORmZmbWWubt26Yc2aNZg1axY+/PBDJCQkYOHChWwrqAK5V7s7rrTH1DxmvoonbMwMIKD29ZeDQ54iX5GNC9NeMEruQS2KQHm1A2b2pPYKp0vExZr3wTgN1aygtrk0wM/MAJLbS14stSH1aE0wIJktBYm8TZPBAAB46qmn8NRTTzV635IlSxpsGz16NPbt2+fmUWlPfk2adZQblgnUZgaouUxAeix3ZDKQ/3BH1klrZNcsE4jXYGZAYIAeRr0O1U4XrFUMBnhLfpkNTpcIvU7g+yA1SV5KJZNPBEm75IBQqc2BUpsDIUY9hvdovO4WEXmOthadks8pLJevtKv/RUFJSVMzM0AerxuCF+Q/LCHerRmg1SuyYUE1SwVYN8BrlLoVoSZ2U6Em1Q0U6XWCWwL+5F/Cggww1ql1M7p3DEwGvRdHREQAgwHkZcqVdrdkBqh/wiZnMrgjeEH+I9oNgabWkDMDtLpWW84GsFY6vDwS7cpRigfyPZCaVreuTnSIkYEjgiAI9WpJjO/DJQJEvoDBAPKq2poB7ssMKLU5UGV3qvKYrBlAQG1xLE9nBig1A7SaGRDIzABvYycBao26ywRizPy8JInc8lmvE3Bj7w5eHg0RAQwGkJflu/HkOiywNiWtQKVCb/LjXFopmbQl2g2BppaIolinZoA2T8SUzAAGA7xG60tVqHWigo2Qi8SztgTJ5LkwpEskIoL5PYrIF6haQFBuxXc5bDbPptuS99kcTpRWSem+Fje06hMEAdGhRmSXVCG/1IaOEVdedE2+EszWgtoWFmhAgF6A3SmisLwaCSrMrZZYKx2orAk8aPWqbG3NAC4T8JYcZgZQKxj0OkQGB6Cw3M5OAqTomxCGTcfyMHlQR28PhYhqqBoMmDZt2mX3CxVFkb1GNaaoXLq6Z9AJypd8tVlCTVIwQIW13VV2J8ps0klIFDMDNE0QBESHmJBjrUJBmWeCAfJJWGRwAAIDtFl0yWySawYwM8Bb8qw1bQUZDKAWRIcYpWAAlwlQjadv7IlxybG4ulO4t4dCRDVUXyYgiuJl3Uh75BP0qBCj2wJBahYRlJcIGPU6mE2a7cpJNeS5lV/umaym7JJKAECcBtsKysyBzAzwthyN162g1pNTwpkZQLLAAD0Gdo7gxT8iH6LqGY3L5VLz4aidc2fxQJn8ZeSiCpkBdYsH8oOMlI4CpZ4JBihrtTVcxT0sSK4ZwGCAt+SWcJkAtc49gzsiK7cAN/aO8fZQiIioCby8SV4jn1y7sxifqpkBSltBpjxSbTtMtYpTtqT2iiwzA1hA0DvKbQ6U1iyVYmtBasltA+KhP78fnSK1+55FROTrfKabwCeffIKFCxd6exjkQbXF+Nx3ch2jYj94pfMBiwcSalskFagwt1ojR+OdBAAgrKabAJcJeIcckAox6pXODkREROS/fCYY8NJLL2HWrFneHgZ5kHxFNcqNJ9dKZoAK67prlzUwM4Bqg1hqZJ20RjZbutVmBrCAoFcoSwQ0PAeJiIjaE58JBpD21F2D7y4WZV23GssE5GUNzAygOjUDPLRMIFdeJqDhtdpyzYBSLhPwihzOQSIionaFwQDyGvlKu1trBtRkHaiSGeCBZQ3kP2rrUXiqmwCXCdTWDOAyAW/IZVtBIiKidoXBAPKaAg+swbfU9DcuLK+G03VlLSzzPdD9gPyHJUS9ehQtqah2oKQmNV7LywRqawYwM8Ab5OwULhMgIiJqHxgMIK/J90B1/qhgIwQBcIlSQOBKeGJZA/mPup0qRPHKAk0tkYsHar1wmxwMqLK7UO1gK1tPq21vyWAAERFRe8DWguQ18sm5OzMDDHodIoONKCyvRkG5DTHmy38ueZmAhd0ECLXBAIdLxH2Ld0KvE9z2XHL1fC1nBQBAaGDtR1ZplZ1ZOh4m1wxgW0EiIqL2gcEA8oqKagcq7U4A7r/SbgmVggF3fbT9ik7Y5BMyZgYQAJgMeiRGBSOzsAK/ZhR65DmT4sI88jy+Sq8TEGoyoMzmgLXKwWCAhynLBJgZQERE1C6oGgzIzMy87GNdLqZ8aol8lT0wQIdgo96tzzW0axSO55ahotp5xY/VOSoIHa4gu4Dal+W/H4a9Z4s88lwGnYCRPSweeS5fFhYoBQNYN8CznC4ReaU1BQQ1nqFCRETUXqgaDOjWrdtlHyuKIgTBfWm25Fvy6xQPdPfr/ufJ/fD/bugBpwrruuPDA2HQs9QGSTpGBKFjRJC3h6Ep5sAAoKQK1kp2FPCkgjIbnC4ROgGIYUYGERFRu6BqMMDdRbTI/d7fdArHzwk4tekUdG486T1bUAHAvW0FZYIgIDE62O3PQ0TuFxYkfWwxM8Cz5HoBllATA6JERETthKrBgM2bN6v5cOQFCzefAqDH2vOnPPJ8TDclorYwK+0FmRngSblWLhEgIiJqb1QNBowePVrNhyMvuH9oJ2RmZiIxMRE6nXuv/gTodXjwukS3PgcRtS/mmo4CVmYGeFQOiwcSERG1O+wmQPW8fnsfrFlzBhMn9kFAgHb7mRORbwqryQywMjPAo3JL2FaQiIioveHCPyIi8htKZkAlMwM8Sc4MiGNmABERUbuhambAF1980bonNRgQERGB5OTkK+pAQERE2hIWxJoB3pDLZQJERETtjqrBgGnTprW5TVxycjLmzp2Lu+++W82hEBFRO8SaAd6RU7NMgAUEiYiI2g/VlwmIotim25EjR3DvvffirbfeUnsoRETUzoQp3QQYDPAkLhMgIiJqf1QNBrhcrlbd7HY7cnNzsW7dOtxzzz0QRRGvvPIKDh48qOZwiIionamtGcBlAp5SUe1QlmXEMjOAiIio3fBKAUG9Xo+YmBiMHz8eK1aswAsvvACn04lFixZ5YzhEROQnlJoBNmYGeEqu1QYACDbqYTaxCREREVF74RPdBF5++WUYjUZs2bLF20MhIiIfFsbMAI/LKaktHtjWukBERETku3wiGBASEoKkpCScP3/e20MhIiIfVrdmgCiKXh6NNtR2EjB5eSRERESkJp8IBgBAYGAgqqqqvD0MIiLyYeaaYIBLBMqrnV4ejTaweCAREVH75DOL/7KyshAZGentYRARkQ8LDNAhQC/A7hRRWmVHKNewN+lsQTk+3HwSxRVXVl/hRF4ZABYPJCIiam984lvUrl27kJWVhZtuusnbQyEiIh8mCALMgQEoLK+GtdKB+HBvj8g3rTucg//+5oDSBUANPWNCVXssIiIi8j6vBwOOHTuGqVOnQhAE3HXXXd4eDhER+ThzoAGF5dUorWJHgUs5nC78ZV06/vbzaQDA4C6RuOuajhBwZYX/woMCkNI3Vo0hEhERkY9QNRjw+uuvt2o/h8OBoqIipKWlYceOHXC5XEhOTsa0adPUHA4REbVDtUUE2VGgrjxrFWZ8tR+7zhQCAB4f1Q3PT0hCgN5nygMRERGRD1E1GDBnzpw2tR2SK0H3798fq1evRmAg1yMSEVHzzHJ7QWYGKHacKsAzy/cjv8yGUJMBf5kyABP6x3t7WEREROTDVA0GJCYmtioYoNfrERERgeTkZNx6662YMmUK9Hq9mkMhIqJ2Ss4MsDIzAC6XiEU/n8I769LhEoGkODM+evAadOf6fiIiImqBqsGAM2fOXPaxZWVlcLlcCAsLU29ARETU7iiZAZXazgwoqbDjuW/SsPFoHgDg7ms64c+T+yHIyOA6ERERtczrBQRl3bp1Q1FRERwOXukhIqKmhQWpXzNg87E8vPH9EdgcLtUe88qIqKzU4+2jPwNNFP+zVtlRWuWA0aDD67f3xX8N7dympXpERESkbT4TDABqawgQERE1xR01A/6xLQOn88tVezx1CICtqtk9OkcF4eMHB6NfR/ZYJCIiorbxqWAAERFRS9TuJuB0idifWQwA+OCBQegcGazK414Jh8OBbdu3YeSIkTAYGv+o1gkCrooLhcnAZQFERETUdgwGEBGRX1G7ZsCxHCvKbA6EmgyY0C8eep33U+3tdjvOhwIDOoUjICDA28MhIiKidojNh4mIyK/U1gxQJxiw92wRAGBQYoRPBAKIiIiIPIHBACIi8iu1NQPUWSaw54wUDBjSJUqVxyMiIiLyBwwGEBGRX6mtGaBuZsCQrpGqPB4RERGRP2AwgIiI/IocDLBWXnlmQHZJJbKKK6HXCRjYOeKKH4+IiIjIX6haQHDs2LGXfWxJSYmKIyEiovYqLEj66Kq0O2F3uhCgv/y4trxEIDnejBATa+oSERGRdqj6zeenn36CIAgQRfGyjhcEFm4iIqLmhdY5aS+rciAyxHjZj6UsEWC9ACIiItIYVYMBDz/8ME/oiYjIrQx6HYKNelRUO2Gtsl9RMGD3mUIAwOAurBdARERE2qJqMGDJkiVqPhwREVGjwgIDUFHtROkVdBQoszlwNNsKgMUDiYiISHtYQJCIiPyO0l6w8vI7CqRlFsMlAh0jghAfHqTW0IiIiIj8AoMBRETkd8KCajoKXEFmwJ6z0hIBZgUQERGRFjEYQEREfkfJDKi6/MyA2uKBDAYQERGR9jAYQEREficsUMoMuNyaAU6XiP2ZxQCAwewkQERERBrEYAAREfmdK60ZcCzHijKbA2aTAb3jzGoOjYiIiMgvaC4YUFRUhKlTpyI8PBzh4eGYOnUqiouLmz1m5cqVuPnmm2GxWCAIAtLS0jwyViIiapxcM+ByMwPkJQIDEyOg17ElLhEREWmP5oIBDzzwANLS0rB27VqsXbsWaWlpmDp1arPHlJeXY+TIkViwYIGHRklERM250poBe87I9QK4RICIiIi0yeDtAXjS0aNHsXbtWuzcuRPXXXcdAOCTTz7B8OHDkZ6ejt69ezd6nBwsOHPmjKeGSkREzaitGXB5wQCleCA7CRAREZFGaSozYMeOHQgPD1cCAQAwbNgwhIeHY/v27V4cGRERtUVtzYC2LxO4UFyJrOJK6HUCBnaOUHlkRERERP5BU5kBOTk56NChQ4PtHTp0QE5OjqrPZbPZYLPZlN+tVisAwG63w26//FZY7iaPzZfHSNQczmFtCAmQ1vlbq6rb/Fr/euoiACApLhRGneiTc4XzmPwd5zC1B5zH5K9aO2fbRTBgzpw5mDt3brP77N69GwAgCA0LRYmi2Oj2KzF//vxGx7R+/XoEBwer+lzusGHDBm8PgeiKcA63bxmlAGBAboEVa9asadOxKzN0AHSIdpW0+VhP4zwmf8c5TO0B5zH5m4qKilbt1y6CATNmzMB9993X7D5du3bFwYMHkZub2+C+ixcvIjY2VtUxvfjii5g9e7byu9VqRefOnZGSkoKwsDBVn0tNdrsdGzZswPjx4xEQEODt4RC1GeewNpzIK8N7v22HU2/ExIk3tunYv320A0Ap7r5hICb2j3PPAK8Q5zH5O85hag84j8lfyVnpLWkXwQCLxQKLxdLifsOHD0dJSQl27dqFa6+9FgDw66+/oqSkBCNGjFB1TCaTCSaTqcH2gIAAv3gz8ZdxEjWFc7h9iwoNAiC1FjQYDK3O7iqzOXAspxQAcF2PGJ+fI5zH5O84h6k94Dwmf9Pa+aqpAoLJycm45ZZbMH36dOzcuRM7d+7E9OnTceutt9brJJCUlIRVq1YpvxcWFiItLQ1HjhwBAKSnpyMtLU31OgNERNQ6cgFBh0tEpd3Z6uPSMovhEoGOEUGICw901/CIiIiIfJ6mggEAsGzZMvTv3x8pKSlISUnBgAED8OWXX9bbJz09HSUlJcrv3333HQYNGoRJkyYBAO677z4MGjQIixYt8ujYiYhIEmzUQ6+TsgFKq1rfUWDP2UIAbClIRERE1C6WCbRFVFQUli5d2uw+oijW+33atGmYNm2aG0dFRERtIQgCzIEGFFfYYa20IzasdVf5954tAgAM6cJgABEREWmb5jIDiIiofQgLlNbDWVuZGeB0idifWQwAGNwlyl3DIiIiIvILDAYQEZFfkusGWKta10v3WI4VZTYHzCYDeseZ3Tk0IiIiIp/HYAAREfklOTOgtTUD9pyRlggMTIxQ6g0QERERaRWDAURE5JeUzIDK1mUG7FHqBXCJABERERGDAURE5JfCgtqWGbD3DDsJEBEREckYDCAiIr/UlpoBF4orcaGkCnqdgIGdI9w8MiIiIiLfx2AAERH5pdqaAS0HA+QlAn3iwxBi0lxXXSIiIqIGGAwgIiK/VFszoOVlAvISgcFduESAiIiICGAwgIiI/FRtzYDWZwawXgARERGRhMEAIiLyS2E1mQEtFRAsszlwNNsKgJ0EiIiIiGQMBhARkV8y19QMaKmAYFpmMVwi0DEiCHHhgZ4YGhEREZHPYzCAiIj8Um0BweYzA1buOw+ASwSIiIiI6mIwgIiI/FJtAcGmMwO2HL+IlfuzIAjAIyO6emhkRERERL6PwQAiIvJLcgHB8monHE5Xg/vLbA68tPIQAGDaiK64JpGZAUREREQyBgOIiMgvyZkBgHTif6l31qUjq7gSnSKD8N8pvT05NCIiIiKfx2AAERH5pQC9DkEBegAN6wbsOVOIz3ecAQDMv6s/QkyGSw8nIiIi0jQGA4iIyG/J2QEldeoGVNmdeP7bgxBF4J7BnXB9rxhvDY+IiIjIZzEYQEREfkuuG1A3M+CDTSdx6mI5YswmvDKpj7eGRkREROTTGAwgIiK/pXQUqJIyAw5fKMGiLacAAG/c0RfhwQFeGxsRERGRL2MwgIiI/FZYYG1mgMPpwvPfHoTDJWJCvzjc0i/ey6MjIiIi8l0MBhARkd9SMgMq7fj71gz8lmVFWKABc+/o6+WREREREfk2llcmIiK/JdcMOJRVgjWHsgEAr9zaBx3Mgd4cFhEREZHPYzCAiIj8lpwZsGp/FgBgVE8L7hncyZtDIiIiIvILXCZARER+S64ZAABBAXrMv6s/BEHw4oiIiIiI/AODAURE5LfCAmsT3P7n5t7oHBXsxdEQERER+Q8GA4iIyG/1iAkFAAzuEolHRnT17mCIiIiI/AhrBhARkd8a3iMaK58ageS4MOh1XB5ARERE1FoMBhARkd8SBAHXJEZ6exhEREREfofLBIiIiIiIiIg0hsEAIiIiIiIiIo1hMICIiIiIiIhIYxgMICIiIiIiItIYBgOIiIiIiIiINIbBACIiIiIiIiKNYTCAiIiIiIiISGMYDCAiIiIiIiLSGAYDiIiIiIiIiDSGwQAiIiIiIiIijWEwgIiIiIiIiEhjDN4egFaIoggAsFqtXh5J8+x2OyoqKmC1WhEQEODt4RC1GecwtQecx+TvOIepPeA8Jn8ln3PK56BNYTDAQ0pLSwEAnTt39vJIiIiIiIiIqL0rLS1FeHh4k/cLYkvhAlKFy+XChQsXYDabIQiCt4fTJKvVis6dO+PcuXMICwvz9nCI2oxzmNoDzmPyd5zD1B5wHpO/EkURpaWlSEhIgE7XdGUAZgZ4iE6nQ6dOnbw9jFYLCwvjmx75Nc5hag84j8nfcQ5Te8B5TP6ouYwAGQsIEhEREREREWkMgwFEREREREREGsNgANVjMpnw2muvwWQyeXsoRJeFc5jaA85j8necw9QecB5Te8cCgkREREREREQaw8wAIiIiIiIiIo1hMICIiIiIiIhIYxgMICIiIiIiItIYBgOIiIiIiIiINIbBAFJ89NFH6NatGwIDAzF48GD88ssv3h4SUZPmz5+PoUOHwmw2o0OHDpg8eTLS09Pr7SOKIubMmYOEhAQEBQVhzJgxOHz4sJdGTNS8+fPnQxAEzJw5U9nGOUy+LisrCw899BCio6MRHByMgQMHYu/evcr9nMPk6xwOB1555RV069YNQUFB6N69O15//XW4XC5lH85jaq8YDCAAwIoVKzBz5ky8/PLL2L9/P66//npMmDABmZmZ3h4aUaO2bNmCp59+Gjt37sSGDRvgcDiQkpKC8vJyZZ+3334b7777Lj744APs3r0bcXFxGD9+PEpLS704cqKGdu/ejcWLF2PAgAH1tnMOky8rKirCyJEjERAQgB9//BFHjhzBX//6V0RERCj7cA6Tr3vrrbewaNEifPDBBzh69Cjefvtt/OUvf8H777+v7MN5TO2WSCSK4rXXXis+8cQT9bYlJSWJL7zwgpdGRNQ2eXl5IgBxy5YtoiiKosvlEuPi4sQFCxYo+1RVVYnh4eHiokWLvDVMogZKS0vFXr16iRs2bBBHjx4tPvvss6Iocg6T73v++efFUaNGNXk/5zD5g0mTJomPPvpovW133XWX+NBDD4miyHlM7RszAwjV1dXYu3cvUlJS6m1PSUnB9u3bvTQqorYpKSkBAERFRQEAMjIykJOTU29em0wmjB49mvOafMrTTz+NSZMm4aabbqq3nXOYfN13332HIUOG4J577kGHDh0waNAgfPLJJ8r9nMPkD0aNGoXU1FQcP34cAHDgwAFs3boVEydOBMB5TO2bwdsDIO/Lz8+H0+lEbGxsve2xsbHIycnx0qiIWk8URcyePRujRo1Cv379AECZu43N67Nnz3p8jESN+frrr7Fv3z7s3r27wX2cw+TrTp8+jY8//hizZ8/GSy+9hF27duEPf/gDTCYTHn74Yc5h8gvPP/88SkpKkJSUBL1eD6fTiXnz5uH+++8HwPdiat8YDCCFIAj1fhdFscE2Il80Y8YMHDx4EFu3bm1wH+c1+apz587h2Wefxfr16xEYGNjkfpzD5KtcLheGDBmCN998EwAwaNAgHD58GB9//DEefvhhZT/OYfJlK1aswNKlS/HVV1+hb9++SEtLw8yZM5GQkIBHHnlE2Y/zmNojLhMgWCwW6PX6BlkAeXl5DaKgRL7mmWeewXfffYfNmzejU6dOyva4uDgA4Lwmn7V3717k5eVh8ODBMBgMMBgM2LJlCxYuXAiDwaDMU85h8lXx8fHo06dPvW3JyclK8WG+D5M/+J//+R+88MILuO+++9C/f39MnToVs2bNwvz58wFwHlP7xmAAwWg0YvDgwdiwYUO97Rs2bMCIESO8NCqi5omiiBkzZmDlypXYtGkTunXrVu/+bt26IS4urt68rq6uxpYtWzivySeMGzcOhw4dQlpamnIbMmQIHnzwQaSlpaF79+6cw+TTRo4c2aCl6/Hjx9GlSxcAfB8m/1BRUQGdrv4pkV6vV1oLch5Te8ZlAgQAmD17NqZOnYohQ4Zg+PDhWLx4MTIzM/HEE094e2hEjXr66afx1Vdf4d///jfMZrMSsQ8PD0dQUJDSr/3NN99Er1690KtXL7z55psIDg7GAw884OXREwFms1mpcSELCQlBdHS0sp1zmHzZrFmzMGLECLz55pu49957sWvXLixevBiLFy8GAL4Pk1+47bbbMG/ePCQmJqJv377Yv38/3n33XTz66KMAOI+pnfNiJwPyMR9++KHYpUsX0Wg0itdcc43Soo3IFwFo9PbZZ58p+7hcLvG1114T4+LiRJPJJN5www3ioUOHvDdoohbUbS0oipzD5Pv+85//iP369RNNJpOYlJQkLl68uN79nMPk66xWq/jss8+KiYmJYmBgoNi9e3fx5ZdfFm02m7IP5zG1V4IoiqI3gxFERERERERE5FmsGUBERERERESkMQwGEBEREREREWkMgwFEREREREREGsNgABEREREREZHGMBhAREREREREpDEMBhARERERERFpDIMBRERERERERBrDYAARERGRB82ZMweCIGDatGneHgoREWkYgwFERETNmDZtGgRBaPH2008/eXuofi0tLQ1z5szBkiVLLvsxlixZorweLZFf1zFjxlz28xEREfkzg7cHQERE5A/CwsLQv3//Ju8PDw/34Gjan7S0NMydOxejR49u91fMLRYLevfujfj4eG8PhYiINIzBACIiolYYNGgQr/6TKmbMmIEZM2Z4exhERKRxXCZAREREREREpDEMBhAREblBdnY2nnvuOSQnJyM4OBhmsxkDBw7E3LlzUVJS0ugxY8aMgSAIWLJkCXJycjBjxgx0794dJpMJAwcOrLfvuXPnMGvWLPTp0wehoaEICQnB1Vdfjddffx2lpaVNjsvpdGLp0qWYMGECYmNjYTQakZCQgFGjRmHBggXIz8+vt//WrVvx/PPP47rrrkNCQgKMRiMsFgvGjx+P5cuXN/k8drsdH3zwAUaOHInIyEgEBASgQ4cOGDBgAB5//HFs2rRJ2bdr16743e9+BwDYsmVLg3oMZ86caeFfW31OpxOffvopxowZg6ioKJhMJiQmJuKhhx7C/v37Gz1GrlkwZswYuFwuLFq0CMOGDUNERAQEQUBaWhqApgsIdu3a9bLrU3z77be45ZZbEBMTA6PRiPj4eNx1111NZrP89NNPEAQBXbt2BQB8//33GDduHCIjIxEcHIxBgwZh0aJFl/mvR0RE/oDLBIiIiFS2Y8cO3HrrrSgsLITBYEC/fv1gt9tx8OBBHDhwAJ9//jnWr1+Pnj17Nnr8qVOnMGDAABQWFiI5ORl9+/aF0WhU7v/uu+/wwAMPoLy8HEajEd26dQMAHD58GAcPHsTy5cuRmpqKhISEeo9bWFiIyZMn45dffgEAxMbGYuDAgbh48SJ+/fVXbNu2DUlJSZg8ebJyzOTJk1FQUIDIyEjExcUhISEBFy5cwMaNG7Fx40asX78en332Wb3ncblcmDhxIjZu3AgASExMRK9evVBcXIxTp07h0KFDKCsrw9ixYwEAQ4cOhdFoxIkTJxqtzRAYGHgZr8LlKy8vxx133IHU1FQAQJcuXdCjRw+kp6dj2bJl+Prrr/HRRx/h97//faPHi6KIe+65BytXrkSnTp1w1VVX4ezZsy0+79ChQ9GpU6dG77NarTh06FCD7S6XCw8//DCWLVsGAIiPj8fAgQNx+vRprFq1CqtWrcIrr7yCN954o8nn/fOf/4xXX30VMTEx6NmzJzIyMpCWloYnn3wSZ86cwYIFC1ocOxER+SGRiIiImvTII4+IAMTRo0e3av+CggIxLi5OBCCmpKSIOTk5yn3p6eliv379RADigAEDRIfDUe/Y0aNHiwBEvV4vjhs3Tjx//rxyX0VFhSiKopiWliYGBgaKAMSXX35ZLC0tVfbJysoSU1JSRADi2LFjG4xt3LhxIgAxPj5eXLduXb37ysrKxL///e/inj176m3/5JNPxJMnTzZ4rJ07d4o9e/YUAYjffPNNvftWr14tAhA7duwo7t+/v959LpdL/Omnn8Tly5fX2/7ZZ5+16d+5MfJjtObrTXOv6/Tp00UAYlRUlLhx40Zle2VlpfjMM88or9GOHTsafX69Xi9GRESIP/74o3Kfw+EQbTabKIqi+Nprr4kAxEceeaRVf5fNZlPmxrBhw5S5IIqiOG/ePBGAGBgYWO/f1OFwiAsWLFD+Pf75z3/We8zNmzeLAMSAgAAxMDBQ/Pzzz0WXyyWKovQayY+r0+nE06dPt2qcRETkXxgMICIiaoZ80tjc7bXXXlP2l0+iIiMjxZKSkgaPd/ToUVGn04kAxBUrVtS7Tz7hi4mJEYuLixsdz6RJk0QA4h/+8IdG7y8pKRETEhJEAOKvv/6qbP/++++Vk7+DBw9exr9EQ+vXrxcBiBMnTqy3ff78+SIAcebMma1+LLWDAa29Xfp8Z8+eFfV6vQhA/PLLLxt9nhEjRogAxAkTJjT5/MuWLWtynG0NBjz00EMiALF79+5iXl6esr28vFyMiIgQAYhvvPFGo8fef//9IgCxT58+9bbLwQAA4pw5cxo9tn///iIAceHCha0aJxER+RcuEyAiImqF5loLJiYmKv/9/fffAwB+//vfIywsrMG+SUlJuP3227F69Wr88MMPuPfeexvsM2XKlEZbFVqtVqxduxYA8OSTTzY5zvHjx+Pzzz9Hamoqrr32WgDAN998AwC44447mm2R2Jj09HR88803OHDgAAoKClBdXQ0AsNlsAIC9e/fW279Lly4AgPXr1yM3NxexsbFtej41jBw5stn7T5w4gby8vAbb165dC6fTiYSEBNx///2NHvvHP/4RkydPRmpqKmw2G0wmU737zWZzo6/r5XjttdewdOlSREVF4ccff0RMTIxy39atW1FcXAyj0Yhnnnmm0eOff/55LF++HEeOHEFGRoaypKSupo4dPnw4Dh06hBMnTqjytxARkW9hMICIiKgVWtta8NixYwDQ7An3gAEDsHr1amXfS/Xt27fR7b/99hucTicA4PHHH2/y8eX16efOnVO2yevNWzpJvtSf/vQnzJs3Dy6Xq8l9CgoK6v0+efJk9O7dG0eOHEFiYiLGjBmDUaNGYfjw4RgxYgSCg4PbNIbLsXXr1mbvnzZtGj7//PMG2+XXpE+fPtDr9Y0eO2DAAABAdXU1Tp8+jeTk5Hr39+7dGwbDlX/F+vzzz/H666/DZDJh9erVuOqqqxoda2JiYqPBI0CaS3q9Hk6nE8eOHWsQDLBYLIiKimr0WDmIU1ZWdqV/ChER+SAGA4iIiFRktVoBAHFxcU3uEx8fX2/fS4WEhDS6vaioSPnvbdu2tTiWioqKBuOKiIho8TjZN998gzfeeAOCIOCll17ClClT0L17d5jNZuh0Opw+fRo9evSAw+God1xQUBB++eUXzJs3D8uXL8f69euxfv16AEBwcDAeeOABzJ8/HxaLpdVj8ZS2vH5196+rqdevLTZv3ozp06dDEAR89tlnuP766y9rrAaDARaLBbm5uW0eq04nNZ1qLhBERET+i8EAIiIiFYWFhaGoqAg5OTlN7pOdna3s2xahoaEApBO4tl6tlZ+ruLi41cfIXQJmzpyJefPmNbj/0oyAumJiYvDee+/hvffeQ3p6OrZv344NGzZg1apV+Pvf/47Dhw/jl19+afLqu7fI/06tef3q7q+mo0eP4q677oLdbsebb77Z5HKF1ozV4XAo7SLdMVYiIvJfOm8PgIiIqD1JSkoCgEbbwMnk+y5NL29J3759odPpUF5e3mSv+6bIqe2tySiQnTp1CgAwZsyYRu/fvn17qx6nd+/e+N3vfoevvvoK27dvhyAI2LFjB/bs2aPsIwhCq8flTvLrd+TIEWVJxqXk189oNKJ79+6qPn9ubi4mTpyI4uJiPP7443jxxRdbHGtmZmaTWSZ1/462zjciImrfGAwgIiJS0a233goAWLx4caMnaMePH8d3330HAJg0aVKbHttisWDs2LEA0Gzf+MZMmTIFAPDvf/8bv/32W6uOkdf2170SLqusrMQHH3zQpjEAUu0FeX17VlZWg+eqrKxs82OqacKECdDr9bhw4QKWL1/e6D5vv/02AOCmm25qUDzwSlRUVOC2227DmTNnkJKSgo8//rjZ/UeNGoWIiAhUV1dj4cKFzY61T58+6Nq1q2pjJSIi/8dgABERkYqeeOIJxMXFoaioCPfeey9yc3OV+06cOIF77rkHTqcTV199Ne666642P/7bb7+NoKAgrFq1Cg8//HC9E2pASgv/+eef8eijj9a7b9KkSRg7dizsdjsmTJiAjRs31juuvLwc//jHP+p1BpAzAt588816xQ6zs7MxefJknD9/vtEx/vWvf8Vbb72FjIyMeturq6vx1ltvobi4GDqdDtdcc41yX8+ePQEAhw8frvdv5mmdO3fGY489BgB49tlnkZqaqtxXVVWF2bNnY9u2bdDr9Xj11VdVe16Xy4UHH3wQu3fvxoABA/Cvf/2rxSKEwcHB+OMf/wgAmDdvHr7++mvlPqfTiXfeeQdfffUVAGDu3LmqjZWIiNoH1gwgIiJSUVRUFFauXIlJkyZh3bp16Ny5M/r27QuHw4EjR47A5XKha9eu+Ne//nVZ6+UHDRqElStX4oEHHsCXX36JpUuXomfPnoiOjkZpaSlOnTqFqqoqAFIngLpWrFiB22+/HTt27MD48eMRFxeHzp07Iz8/H+fOnYPD4cCqVaswePBgAFILvRUrViAzMxP9+vXDVVddBaPRiMOHD8NgMODDDz9UTpzrOnfuHP7v//4PL7zwAmJjY9GpUyc4nU5kZGSgpKQEALBgwYJ6V6oHDhyI/v3749ChQ+jRowf69OmjZAt8/fXXzRbJU9u7776LkydPYtOmTbjpppvQtWtXxMTEID09HVarFXq9Hh999BGGDRum2nNmZmZi9erVAKSWjRMmTGhy3/fffx+DBg0CILUO/O233/DVV1/h/vvvx3PPPYdOnTrh1KlTSk2HV155RckMISIikjEYQEREpLLhw4fjt99+wzvvvIM1a9bg2LFj0Ol06NevH+68807MmjWryVZwrXHLLbcgPT0dH374IdasWYPjx4/j7Nmz6NChAwYOHIjRo0fjjjvuQJcuXeodZ7FY8PPPP+OLL77AsmXLcODAAaSlpSEmJgbDhg3D7bffXq9qfXx8PH799Ve8+uqrWLt2LU6ePAmLxYIpU6bgpZdegtlsbnR8Tz75JDp06ICffvoJx48fx9GjR+FwOBAXF4cJEybg6aefxqhRo+odIwgC1qxZg1deeQWbNm1CWloa7HY7ACjBDU8JCQnBunXrsGTJEnz55Zc4cOAAsrKyEBsbi9tuuw2zZ8+ul9WgtvT0dKSnpzd5vxxQAaSK/8uWLcOdd96JTz75BHv37sW+ffsQHR2NO++8E8888wxuvPFGt42ViIj8lyCKoujtQRARERERERGR57BmABEREREREZHGMBhAREREREREpDEMBhARERERERFpDIMBRERERERERBrDYAARERERERGRxjAYQERERERERKQxDAYQERERERERaQyDAUREREREREQaw2AAERERERERkcYwGEBERERERESkMQwGEBEREREREWkMgwFEREREREREGsNgABEREREREZHGMBhAREREREREpDH/HwNKAfqoS5RsAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x1100 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=3, ncols=1, figsize=(10, 11))\n",
    "fig.tight_layout()\n",
    "\n",
    "series = ['HUFL','HULL','LUFL','LULL','MUFL','MULL','OT']\n",
    "series_idx = 3\n",
    "\n",
    "for idx, w_idx in enumerate([200, 300, 400]):\n",
    "  axs[idx].plot(y_true[series_idx, w_idx,:],label='True')\n",
    "  axs[idx].plot(y_hat[series_idx, w_idx,:],label='Forecast')\n",
    "  axs[idx].grid()\n",
    "  axs[idx].set_ylabel(series[series_idx]+f' window {w_idx}', \n",
    "                      fontsize=17)\n",
    "  if idx==2:\n",
    "    axs[idx].set_xlabel('Forecast Horizon', fontsize=17)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we compute the test errors for the two metrics of interest:\n",
    "\n",
    "$\\qquad MAE = \\frac{1}{Windows * Horizon} \\sum_{\\tau} |y_{\\tau} - \\hat{y}_{\\tau}| \\qquad$ and $\\qquad MSE = \\frac{1}{Windows * Horizon} \\sum_{\\tau} (y_{\\tau} - \\hat{y}_{\\tau})^{2} \\qquad$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE:  0.26096806135482414\n",
      "MSE:  0.18279484416711375\n"
     ]
    }
   ],
   "source": [
    "from neuralforecast.losses.numpy import mae, mse\n",
    "\n",
    "print('MAE: ', mae(y_hat, y_true))\n",
    "print('MSE: ', mse(y_hat, y_true))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For reference we can check the performance when compared\n",
    "to previous 'state-of-the-art' long-horizon Transformer-based forecasting methods from the [NHITS paper](https://arxiv.org/abs/2201.12886). To recover or improve the paper results try setting `hyperopt_max_evals=30` in [Hyperparameter Tuning](#cell-4).\n",
    "\n",
    "Mean Absolute Error (MAE):\n",
    "\n",
    "| Horizon   | NHITS        | AutoFormer | InFormer | ARIMA \n",
    "|---        |---           |---         |---       |---\n",
    "|  96       |  **0.255**   |   0.339    |  0.453   | 0.301 \n",
    "|  192      |  0.305       |   0.340    |  0.563   | 0.345 \n",
    "|  336      |  0.346       |   0.372    |  0.887   | 0.386 \n",
    "|  720      |  0.426       |   0.419    |  1.388   | 0.445 \n",
    "\n",
    "Mean Squared Error (MSE):\n",
    "\n",
    "| Horizon   | NHITS        | AutoFormer | InFormer | ARIMA \n",
    "|---        |---           |---         |---       |---\n",
    "|  96       |  **0.176**   |   0.255    |  0.365   | 0.225 \n",
    "|  192      |  0.245       |   0.281    |  0.533   | 0.298 \n",
    "|  336      |  0.295       |   0.339    |  1.363   | 0.370 \n",
    "|  720      |  0.401       |   0.422    |  3.379   | 0.478 "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "[Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski (2021). NHITS: Neural Hierarchical Interpolation for Time Series Forecasting. Accepted at AAAI 2023.](https://arxiv.org/abs/2201.12886)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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